System and method for terahertz frequency object and crop contamination and constituent detection and handling

ABSTRACT

An agricultural system with an agricultural harvester has a terahertz sensor. The terahertz sensor includes at least one a terahertz source disposed to direct electromagnetic radiation toward a harvest material of the agricultural harvester. A terahertz detector is disposed to detect the terahertz electromagnetic radiation after the terahertz electromagnetic radiation interacts with the harvest material. A controller is operably coupled to the terahertz detector and is configured to detect a harvest-related parameter based on a signal from the terahertz detector and to perform an action based on a detected parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is based on and claims the benefit of U.S.patent application Ser. No. 16/601,219 filed on Oct. 14, 2019, thecontent of which application is hereby incorporated by reference in itsentirety. The present application also claims the benefit of U.S. patentapplication Ser. No. 17/903,230 filed on Sep. 6, 2022, the content ofwhich application is hereby incorporated by reference in its entirety.

FIELD OF THE DESCRIPTION

This disclosure relates to harvesters. More specifically, the presentdisclosure relates to a Terahertz frequency-based detection system forharvesting operations.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines include harvesters, such as combineharvesters, sugarcane harvesters, cotton harvesters, self-propelledforage harvesters, towed balers, and windrowers. Some harvesters canalso be fitted with different types of headers to harvest differenttypes of crops.

Combine harvesters (also referred to as “agricultural combines”) havegreatly improved the efficiency with which corn, canola, soybeans,wheat, oats, sunflowers, and other crops are harvested, threshed,cleaned, and collected for distribution to consumers. Generally, combineharvesters are relatively complex, self-propelled machines capable ofharvesting large swathes of crop plants as the harvester travels over acrop field, while separating grain from material other than grain (MOG)within the harvester. After cleaning, the harvested grain is deliveredinto a grain storage tank, typically by conveyance through a clean grainelevator. As combine harvesters become increasingly advanced, sensorsubsystems are now integrated into harvesters to measure thecharacteristics related to the crop and/or non-crop materials.

Other harvesters include sugarcane from sugarcane harvesters thatharvest sugarcane fields, cotton harvesters, forage harvesters, haybalers, among others.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

An agricultural system has an agricultural harvester and a terahertzsensor. The terahertz sensor includes at least one a terahertz sourcedisposed to direct electromagnetic radiation toward a harvest materialof the agricultural harvester. A terahertz detector is disposed todetect the terahertz electromagnetic radiation after the terahertzelectromagnetic radiation interacts with the harvest material. Acontroller is operably coupled to the terahertz detector and isconfigured to detect a harvest-related parameter based on a signal fromthe terahertz detector and to perform an action based on a detectedparameter.

This Summary is provided to introduce a selection of concepts in asimplified form that is further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to implementationsthat solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic view of a combineharvester equipped with the radio frequency (RF) grain and massconstituent measurement system, as illustrated in accordance with oneexample.

FIG. 2 schematically illustrates additional components that may beincluded in examples of the RF grain and mass constituent measurementsystem.

FIG. 3 is a flowchart of an example process suitably carried-out by acontroller of the RF grain and mass constituent measurement system(FIGS. 1 and 2 ) to determine multiple parameters (e.g., grain mass,moisture content, and/or constituent content levels) of a grainprocessed by the combine harvester shown in FIG. 1 .

FIG. 4 graphically plots RF characteristics (here, expressed in terms ofphase shift) over a tested frequency range for a number of tested grainsamples, which may be utilized by the controller in determining grainmass and a first constituent content (here, oil content) in examples.

FIG. 5 graphically illustrates an example of an RF sensor reading (here,measured in terms of wave amplitude or magnitude) of a tested grainsample over a predetermined frequency range, which may further beutilized by the controller in determining grain mass and a firstconstituent content of a harvested grain.

FIG. 6 is a diagrammatic view of a Terahertz-based sensor in accordancewith one example.

FIG. 7 is a flowchart of an example process suitably carried-out by acontroller to determine one or more parameters of a crop harvestoperation performed by the combine harvester shown in FIG. 1 .

FIG. 8 is a pictorial illustration of one example of a cotton harvester.

FIG. 9 is a partial pictorial, partial schematic illustration of oneexample of a cotton harvester.

FIG. 10 is a partial pictorial, partial schematic illustration of oneexample of a baler.

FIG. 11 is a partial pictorial, partial schematic illustration of oneexample of a sugarcane harvester.

FIG. 12 is a block diagram showing one example of a controller.

FIG. 13 is a flow diagram showing one example of identifying an object,contaminants, constituents, and/or other items.

FIGS. 14A and 14B (collectively referred to herein as FIG. 14 ) show aflow diagram showing one example of controlling an agricultural machinebased on a detected parameter.

FIG. 15 is a diagrammatic view of an example operating in a cloudarchitecture.

FIGS. 16, 17, and 18 show examples of mobile devices that can be used inthe architectures shown in other FIGS.

FIG. 19 is a block diagram showing one example of a computingenvironment that can be used in the architectures shown in the previousfigures.

Like reference symbols in the various drawings indicate like elements.For simplicity and clarity of illustration, descriptions and details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the example and non-limiting embodiments of the inventiondescribed in the subsequent Detailed Description. It should further beunderstood that features or elements appearing in the accompanyingfigures are not necessarily drawn to scale unless otherwise stated.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, instrument systems, methods, andany further application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, steps, or a combination thereof describedwith respect to one example may be combined with the features,components, steps, or a combination thereof described with respect toother examples of the present disclosure.

The International Telecommunications Union (ITU) defines Terahertzelectromagnetic radiation as having a frequency between 0.3 and 3Terahertz. Other sources define the band to include frequencies as lowas 0.1 Terahertz and as high as 30 Terahertz. As used herein, the bandincludes frequencies from 0.1 Terahertz to 30 Terahertz. Terahertzradiation is subject to significant laboratory research and showspromise for agricultural applications. It lies between microwave andinfrared (IR) on the electromagnetic spectrum and provides the advantageof at least partial penetration into objects, but is not consideredionizing radiation, like X-rays. As such, Terahertz radiation does nottrigger a requirement for a safety officer, nor is it subject tosignificant regulations, such as those that apply to X-rays. However,Terahertz electromagnetic radiation does provide improved detectionabilities over optical techniques, IR and ultraviolet (UV). Inaccordance with embodiments described below, Terahertz electromagneticradiation is employed relative to the harvesting operation to betterdetect and react to non-ferrous foreign materials. Terahertzelectromagnetic radiation is also used to detect contaminants such asGMOs, soil/ash, ferrous and non-ferrous metals, chemical residues (e.g.,pesticides), constituents (sugar level, cellulose, etc.), and biotoxins,such as aflatoxin.

Modern combine harvesters are equipped with sensor subsystems formeasuring grain mass and moisture content of harvested grains. In onecommon approach, grain mass is determined by detecting the force atwhich harvested grain strikes a surface positioned within the outlet endof the clean grain elevator. More specifically, the strike force of theclean grain may be measured utilizing a load cell, which is positionedbehind an impact plate struck by the clean grain when flung or thrownfrom the rotating paddles of the grain elevator. As the grain strikesthe impact plate before falling into the clean grain tank, the load celldetects the force at which the grain strikes the impact plate. Thisstrike force, taken in conjunction with the grain elevator speed, isthen utilized to solve for grain mass. Once determined, grain mass canthen be utilized in grain mass flow rate and grain yield calculations,along with other known parameters, such as header width and harvesterspeed.

In addition to grain mass, grain moisture content is also desirablytracked by combine harvesters. The moisture content of a harvested grainimpacts the propensity of the grain to spoil, shrink, or become damagedduring processing and storage. Additionally, variations in grainmoisture content can affect the accuracy of the above-described grainmass measurements and are thus desirably compensated for whencalculating grain mass. For these reasons, combine harvesters are alsocommonly equipped with sensors for estimating the moisture content ofharvested grain. In many instances, grain moisture content is estimatedby measuring capacitance across a known volume of grain, which isdiverted into a test channel or “bypass” from the clean grain stream.The dimensions of the bypass determine the sampled grain volume, andelectrodes (e.g., metal plates) border the sides of the bypass to enablean electrical current to be passed through the sampled grain volume tomeasure capacitance. The capacitance measurement is then converted to amoisture content estimate utilizing a pre-established correlation orequation, noting that electrical conductivity tends to increase (andthus capacitance tends to decrease) as the moisture content of the grainincreases. The capacitance estimate may then be considered by theprocessing architecture or “controller” of the combine harvester to moreaccurately assess grain mass. In other instances, the sampled grainvolume may be weighed, and the weight may be utilized to estimate grainmoisture content (or grain mass) in addition to or in lieu of acapacitance measurement. After estimating grain moisture in this manner,the sampled grain volume may then be returned to the clean grain stream,and such process steps may be repeated to estimate grain moisturecontent on an iterative basis.

While useful in a general sense, the above-described techniques formeasuring grain mass and moisture content remain limited in multiplerespects. Such measurement techniques can be somewhat inaccurate, overlycomplex, and require repeated calibration. Consider, for example, theabove-described technique for estimating the moisture content of a grainprocessed by a combine harvester. The need to repeatedly divert orsequester fractions of the newly-harvested grain from the clean grainstream into a dedicate bypass, measure the capacitance (or weight) ofthe sampled grain, and then return the sampled grain to the clean grainstream is a cumbersome process, which adds undesired cost and complexityto the combine harvester. Further, by the nature of such a quasi-randomsampling process, the grain moisture estimates are taken at discretepoints in time, while interrupting flow of the clean grain stream to alimited extent. In certain instances, the grain moisture estimates maybe temporally offset from the strike force measurements by a significanttime delay, exacerbating inaccuracies in estimating grain mass underchanging grain conditions.

In view of such deficiencies, various alternative techniques formeasuring grain mass and moisture content have been suggested and, incertain instances, implemented. Such alternative techniques are,however, also associated with various shortcomings. As a specificexample, it has been suggested that grain mass may be measured byimpinging harvested grain with high energy, ionizing radiation in theform of x-rays or gamma rays. Such an approach may permit determinationof grain mass and/or moisture content by measuring the degree to whichthe high energy, ionizing electromagnetic (EM) radiation is absorbedinto the harvested grain. This notwithstanding, proposed systemsincorporating high energy emitter and receiver antennae tend to addconsiderable cost and complexity to the sensor subsystem and may besubject to various governmental regulations. Further, as do moreconventional techniques of the type described above, such alternativetechniques for measuring grain mass and moisture content remain limitedin another significant respect, as well—such measurement techniquesprovide little, if any additional useful information pertaining to thecomposition of a harvested grain beyond the moisture content estimateitself.

To overcome the above-noted deficiencies associated with suchconventional grain mass measurement systems, the following disclosesradio frequency-based grain mass and constituent measurement systemswell suited for usage within combine harvesters. As indicated by theterm “radio-frequency based,” the below-described measurement systemsutilize radio frequency (RF) measurements to measure or estimate themass and constituent content(s) of a currently-harvested material; thatis, a material extracted from crop plants ingested and then processed bya combine harvester or other agricultural harvester equipped with themeasurement system. For ease of reference, the RF-based measurementsystems are alternatively referred hereafter to as “RF measurementsystems.” Such terminology denotes that RF measurement system utilizesRF signals in detecting objects, identifying contaminants, and assessingmass and the constituent content level(s), but does not preclude thepossibility that the measurement system may (and often will) utilizeother non-RF input data in rendering such assessments. Further, the term“constituent content” refers to the degree or level to which theharvested material contains at least one constituent, whether moistureor a non-moisture constituent. Examples of non-moisture constituentsinclude protein, cellulose, starch, sugar, or oil contained in theharvested material. Such constituent content levels or quantities willoften be expressed as a volume or weight percentage, such as a protein,cellulose, starch, sugar, or oil percentage (%) by weight; however,other manners in expressing the fractional quantity of a particularconstituent within the harvested material are equally viable. The RFmeasurement system can also identify contaminants such as fungi, mold,toxic weeds, toxic chemicals, hydraulic fluid, etc.

As indicated above, embodiments of the RF measurement system may alsoconsider non-RF sensor input and other non-RF input data in determiningharvested material mass, moisture content, non-moisture constituentmeasurement(s), objects, contaminants, and other harvest-relatedparameters, such as a mass flow rate or overall yield. The measurementsystem may recall from memory and apply pre-established conversionfactors and equations where appropriate; e.g., as utilized in, forexample, converting a measured grain volume (inferred from thebelow-described RF response signals) to grain mass. Clean grain elevatorspeed, or a similar parameter, may be considered when further convertinggrain mass to grain mass flow rate through the combine harvester.Operator input data may also be considered when pertinent, with suchoperator input potentially specifying a particular crop type or cropcategory currently processed by the combine harvester in embodiments.

The RF measurement systems can include any practical number of RFsensors (emitters, receivers, and other associated hardware), whichcollectively form an RF sensor subsystem. In certain embodiments, the RFsensor subsystem can include a single RF receiver and emitter pair,which cycles through multiple fixed frequencies during operation; or,instead, which modulates the emitted RF energy over a predeterminedfrequency range. In other instances, the RF sensor subsystem may containtwo or more RF sensors, with each RF sensor operating at a uniquefrequency or frequency range within the RF domain. When including two ormore RF sensors, the sensor subsystem can utilize real-time data toresolve multiple parameters pertaining to the harvested material, whilepermitting continual, uninterrupted flow of the harvested materialstream. Further, each RF sensor may be optimized to operate at a uniquefrequency or frequency range and tailored to maximize signal-to-noiseratio within its local structural environment; e.g., by customizingantenna shape and dimensions to best suit the region of the combineharvester into which the RF sensor is integrated. Each RF sensor isbeneficially optimized to provide a sensor field-of-view (FOV) orinterrogation area through which substantially all harvested materialcontained in the harvested material stream passes, while furtherminimizing structural interference from any RF-interactive (e.g., metal)components within the interrogation area.

In various implementations in which the sensor subsystem includes atleast first and second RF sensors, a first RF sensor is positioned tocapture RF sensor readings of the harvested material within theharvested material stream at a location in which the material isrelatively compact or aggregated into a consolidated mass; e.g., as whenthe grain is distributed into discrete piles supported by the paddles ofthe clean grain elevator of a combine harvester, or in a bailing chamberof a baler or module forming chamber of a cotton harvester.Additionally, in such implementations, the second RF sensor may bepositioned to capture RF sensor readings of the harvested material whenin a more dispersed distribution, as when airborne and discharged fromthe paddles through the outlet of the clean grain elevator in a combineharvester. In this case, the second RF sensor may be imparted with amore expansive FOV or interrogation area than is the first RF sensor toensure the substantial entirety of the material discharged throughoutlet of the clean grain elevator is impinged by RF energy and capturedby the corresponding RF sensor readings.

The frequencies at which the sensor or sensors within the RF sensorsubsystem operate will vary between embodiments. The operationalfrequencies of the RF sensors can be tailored to best suit a particularsensor location or optimized to elicit a desired signal responseproviding greater resolution for discriminating between the RFcharacteristics stored in memory as “ground truth” testing data.Generally, the RF sensors will operate in the RF domain, which isdefined herein to range from 3 hertz (Hz) to 30 terahertz (THz). Incertain embodiments, the RF sensor(s) within the sensor subsystem willoperate in the microwave band (herein, defined as ranging from 1gigahertz (GHz) to 30 GHz). A tradeoff is encountered as data resolutionand harvested material parameter estimate accuracy tends to increase athigher frequencies (e.g., frequencies exceeding 1 GHz), while the costand complexity of such sensor systems tends to increase at such higherfrequencies. For these reasons, in at least some applications, afrequency or frequency range between 1 and 100 GHz is advantageouslyselected at which to operate each sensor. For example, in suchembodiments, a first RF sensor may operate at a first fixed frequency ora maximum frequency (if emitting RF energy over a frequency range) off1, while a second sensor may operate at a second fixed frequency or aminimum frequency (if emitting RF energy over a frequency range) of f2,with f2 having a value at least twice that of f1.

The RF grain mass and constituent measurement system further includessome form of processing architecture, which is generally referred tohereafter as a “controller.” During system operation, the controllerreceives the RF sensor readings from the RF sensor subsystem andcompares such readings to the information (testing data) stored in an RFcharacteristic database, which resides in a computer-readable memoryonboard the agricultural harvester or otherwise accessible to thecontroller. As indicated above, the RF characteristic database containsRF characteristic testing data observed for tested samples over one ormore tested frequency ranges. Such RF characteristic testing data isadvantageously generated as a ground truth data by gathering RFsignatures or signal response characteristics of a range of sampleshaving known properties (e.g., known grain types, known masses or volumemeasurements, known moisture contents, known constituent contentmeasurements, known contaminants, and known objects) over selectedfrequency ranges encompassing the frequencies at which the RF sensorsoperate. Such RF characteristic testing data can be stored in a memoryaccessible to the controller utilizing any suitable data structure, suchas multidimensional lookup tables. This notwithstanding, the RFcharacteristic testing data is conveniently stored in memory as one ormore RF signal response maps, which graphically plot RF signalcharacteristics of the tested harvested material samples over the testedfrequency range(s). The traces of such maps may be stored as discreteplot points or, instead, stored in the form of a multi-variableequations or formulae.

For increased versatility, such RF signal response maps may be generatedfor harvested material of various types, harvested material categories,various moisture contents, constituent contents, contaminant contents,or the like; and the appropriate RF signal response maps may be recalledby the controller when needed. For example, if determining that thecurrently-harvested grain is corn having a particular moisture contentlevel (e.g., 16%, by weight), the controller may recall the RF signalresponse map (or RF characteristic dataset) for the tested corn sampleshaving the specified moisture content; and then utilize the recalled RFsignal response map to determine grain mass and a non-moistureconstituent content, such as an oil content as set-forth in the examplediscussed below in connection with FIGS. 4 and 5 .

As just indicated, when receiving the RF sensor readings from the RFsensors, the controller then determines mass, the moisture content, anon-moisture constituent content, contaminant content, object presence,and/or other harvest-related parameters corresponding to thecurrently-harvested material based, at least in part, on a comparisonbetween the RF sensor readings and RF characteristic testing data. Inembodiments in which multiple RF sensor readings are captured atdifferent frequencies or frequency ranges, this permits the controllerto solve for multiple unknown parameters using simultaneous equations.Without limitation, ratios, neural networks, or other techniques mayalso be used. Thus, utilizing such an approach, the controller may solvefor harvested material volume (for subsequent conversion to harvestedmaterial mass), moisture content, and a first constituent content (e.g.,protein, cellulose, starch, or oil content) measurement in embodiments.Additional constituent content levels can also be measured, as cancontaminants, and/or object detection, as desired, by gatheringadditional RF sensor readings and utilizing an appropriate number offrequency correlation equations or other techniques.

Multiple different RF properties can be observed and utilized inassessing harvest-related parameters. By way of non-limiting example,the following principally focuses on RF measurements observed asattenuation (decreases in the amplitude or magnitude of RF energy) andphase change (propagation delay of RF energy). While the followingdescription principally focuses on RF signal response measured in termsof RF energy attenuation and phase change, alternative embodiments ofthe RF measurement system may further consider other RF-relatedmeasurements including, but not limited to, polarization, power densitydistribution, reflection, absorption, and back-scattering. Afterdetermining the harvest-related parameters (e.g., mass parameter, andthe constituent quantity estimate, the contaminant estimates, and/or theobject detection) the controller then commands one or more actions basedupon the harvest-related parameters. Such actions may include anycombination of: (i) display of the determined parameters (e.g., as anumerical readout or symbol) on a display device located within anoperator cabin of the combine harvester, (ii) storing the determinedparameters, as time-stamped data, within a memory accessible to thecontroller, (iii) offboarding the determined parameters to centralcontrol source or other remotely-located entity, (iv) commanding anactuator onboard the combine harvester to adjust a component in a mannerresponsive to the newly-determined parameters, and/or (v) generating arecord or radio frequency identification (RFID) tag corresponding to theharvested material, among other things.

By virtue of the above-described functions, embodiments of the RFmeasurement systems achieve multiple notable benefits over conventionalsensor systems utilized to measure grain properties within combineharvesters. Real-time grain assessment is enabled by capturing RF signalresponse readings of the clean grain flow in-situ and withoutinterruption in embodiments in which a first RF sensor captures a firstRF signal response of the clean grain stream at an upstream location,while a second downstream RF sensor captures a second RF signal responseof the clean grain stream at a downstream location, enablingmeasurements of essentially the same body of grain. Calibration demandsare lessened or eliminated, while the accuracy of grain mass and grainmoisture estimates may be maintained, if not enhanced as a result. Usageof sensors operating in the RF domain, and perhaps in the microwave orthe MMW domain, avoids grain exposure to higher energy, ionizing EMradiation. Further, and as particularly useful benefit, information cannow be gathered in real-time regarding the compositional make-up ofgrains processed by a combine harvester. For example, the percentagemake-up of one or more constituents (e.g., protein, cellulose, starch,oil, sugar, or the like) contained within the grain can be determined,opening new possibilities for using such data in various manners.Similar and other advantages are obtained when using other types ofagricultural harvesters as well, some of which are described elsewhereherein.

In some examples, a characteristic database may comprise characteristicsof materials that are considered contaminants. The characteristics maycomprise absorption, reflectance, transmission, backscatter, and otherRF properties as a function of frequency. The non-grain materials maycomprise metals, plastics, wood, weeds, weed seeds, non-grain plantparts, soil, ash, fungi, biotoxins, pesticides, genetically modifiedorganisms (GMOs), hydraulic oil, diesel fuel, or other contaminants.

The following will now describe examples of the RF grain measurementsystem in the context of an example combine harvester, as illustratedand discussed below in connection with FIGS. 1 and 2 . Additionally,methods or processes that may be carried-out by the controller of the RFmeasurement system to determine multiple unknown parameters (grain mass,moisture content, and/or the grain composition of one or morenon-moisture constituents) are further discussed below in conjunctionwith FIG. 3 . Also, examples of RF characteristic testing data that maybe stored in the RF characteristic database as RF response maps arefurther set-forth below in connection with FIGS. 4 and 5 . The followingdescription is provided by way of non-limiting illustration only, andsome other examples (although themselves non-limiting) are discussedbelow in additional FIGS.

Referring to FIG. 1 , an example combine harvester 10 equipped with anRF measurement system 12 is schematically depicted. The combineharvester 10 is presented by way of illustration to establish anon-limiting example context in which embodiments of the RF measurementsystem 12 may be better understood. In further embodiments, the combineharvester 10 may assume other forms and include different combinationsof components suitable for processing crop plants ingested into theharvester 10 when traveling over a field 14. Further, only selectedcomponents of the RF measurement system 12, such as a controller 16, areshown in FIG. 1 for illustrative clarity. Further illustration anddiscussion of the example RF measurement system 12 is provided below inconnection with other FIGS.

The example combine harvester 10 includes a chassis body or main frame18, which is supported by a number of ground-engaging wheels 20. Theground-engaging wheels 20 are powered by a non-illustrated engine anddrivetrain including, for example, an electronically-controlledhydraulic transmission. Atop a forward portion of the main frame 18, acabin 22 encloses an operator station including an operator's seat (notshown), at least one display device 24, and an operator interface 26. Afeederhouse 28 is mounted to a forward portion of the main frame 18 ofthe combine harvester 10 at an elevation generally below the cabin 22.Various harvesting heads or, more simply, “headers” are attachable tothe feederhouse 28 in an interchangeable manner to, for example, allowcustomization of the combine harvester 10 for harvesting a particulartype of crop. An example of one such header, a harvesting platform 30 isshown in FIG. 1 .

As the combine harvester 10 travels over the field 14 in a forwarddirection, the harvesting platform 30 gathers severed crop plants intothe feederhouse 28, which then consolidates the severed crop plants forconveyance (e.g., via a non-illustrated conveyor belt contained in thefeederhouse 28) into the interior of the combine harvester 10. Withinthe combine harvester 10, the crop plants are engaged by a rotating drumconveyor or “beater” 32, which directs the crop plants in a generallyupward direction into a rotary threshing and separating section 34. Therotary threshing and separating section 34 can include variouscomponents for performing the desired functions of separating the grainand chaff from other plant material. The illustrated rotary threshingand separating section 34, for example, includes a rotor or drum 36having threshing features and rotatably mounted in a case or rotorhousing 38. Rotation of the threshing drum 36 within the rotor housing38 causes both grain and chaff to fall through the separation grates ofa concave 40 and into the inlet of a lower grain cleaning section 42.Concurrently, straw and similar MOG is directed toward an outlet end 44of the rotary threshing and separating section 34 and is ultimatelydelivered to another rotating drum or “discharge beater” 46 forexpulsion from an aft end of the combine harvester 10.

Discussing now the grain cleaning section 42 in greater detail, thissection of the combine harvester 10 includes various components adaptedto clean the newly-harvested grain, while separating the chafftherefrom. Such components may include a chaffer 48, a sieve 50, and anynumber of fans (not shown). By action of the grain cleaning section 42,the newly-cleaned grain is directed into a clean grain elevator 52 forconveyance upwardly into a storage reservoir or clean grain tank 53 ofthe combine harvester 10. The path traveled by the clean grain from thegrain cleaning section 42 to the clean grain tank 53 is referred toherein as a “clean grain flow path,” while the grain traveling alongthis flow path is generally referred to as a “clean grain stream.” Anumber of RF sensors 54, 56, which are included in the RF grain mass andconstituent measurement system 12, may be positioned at differentlocations along the clean grain flow path. For example, the RF sensors54, 56 may be strategically positioned to capture RF sensor readings ofthe grain when conveyed through the clean grain elevator 52, asgenerically indicated in FIG. 1 by the placement of the circular symbolsrepresentative of the RF sensors 54, 56. The RF sensors 54, 56 gather RFsensor readings of the newly-harvested grain as the grain is transportedinto the clean grain tank 53. Such RF sensor readings are then utilizedby controller 16 in estimating or calculating grain mass and one or moreconstituent content levels of the grain, as further discussed below inconnection with FIG. 3 .

As the clean grain elevator 52 transports the newly-harvested grain intothe clean grain tank 53, tailings fall onto a return elevator 58extending across a lower portion of the clean grain elevator 52. Thereturn elevator 58 then recycles the tailings back to the inlet of thethreshing drum 36 for further threshing to allow the above-describedgrain processing steps to repeat and maximize the grain yield of thecombine harvester 10. In this manner, the combine harvester 10effectively intakes severed crop plants from the field 14, extractsgrain from the crop plants, cleans the newly-extracted grain, and thenstores the grain in clean grain tank 53 for subsequent unloadingutilizing, for example, an unloading auger 60. Also, during usage of thecombine harvester 10, certain components within the combine harvester 10may be positionally adjusted or the operating parameters of suchcomponents may be modified utilizing any number of actuators 62, such ashydraulic- or electrically-controlled linear or rotary actuators, one ofwhich is generically represented by symbol 62 in FIG. 1 . Such actuators62 may be controlled in response to operator input received via theoperator interface 26 located within the cabin 22, controlled viacommand signals issued by the controller 16 included in the RF grainmass and constituent measurement system 12, or otherwise commanded byanother controller or control unit onboard the combine harvester 10.

Referring now to FIG. 2 , the RF measurement system 12 is shown ingreater detail, as is an upper section of the clean grain elevator 52.Reference numerals are carried-over from FIG. 1 , where appropriate.Note, for example, the inclusion of boxes representative of thecontroller 16, the display device 24, the operator interface 26, and theRF sensors 54, 56, in the schematic of FIG. 2 . In addition to theforegoing components, the RF measurement system 12 may further includeany number of additional non-RF sensors 64 integrated into the combineharvester 10, a wireless datalink 66 having an antenna 68, and acomputer-readable memory 70 storing an RF characteristics database 72.The various data connections between these components are represented inFIG. 2 by a number of signal lines terminating in arrowheads, with suchsignal lines generally representative of any combination of wired orwireless data connections.

The controller 16 of the RF measurement system 12 can assume any formsuitable for performing the functions described throughout thisdocument. The term “controller,” as appearing herein, is utilized in anon-limiting sense to generally refer to the processing architecture ofRF measurement system 12. The controller 16 can encompass or may beassociated with any practical number of processors, control computers,computer-readable memories, power supplies, storage devices, interfacecards, and other standardized components. The controller 16 may alsoinclude or cooperate with any number of firmware and software programsor computer-readable instructions designed to carry-out the variousprocess tasks, calculations, and control/display functions describedherein. Such computer-readable instructions may be stored within anon-volatile sector of the memory 70 along with the below-described RFcharacteristic database 72. While generically illustrated in FIG. 2 as asingle block, the memory 70 can encompass any number and type of storagemedia suitable for storing computer-readable code or instructions, aswell as other data utilized to support the operation of the RFmeasurement system 12. The memory 70 may be integrated into thecontroller 16 in embodiments as, for example, a system-in-package, asystem-on-a-chip, or another type of microelectronic package or module.

The operator interface 26 located within the cabin 22 can be any deviceor group of devices utilized by an operator to input commands into orotherwise control the RF grain mass and constituent measurement system12. In various implementations, the operator interface 26 may beintegrated into or otherwise associated with the display device 24. Inthis regard, the operator interface 26 may include physical inputs (e.g.buttons, switches, dials, or the like) located on or proximate thedisplay device 24, a touchscreen module integrated into the displaydevice 24, or a cursor input device (e.g., a joystick, trackball, ormouse) for positioning a cursor utilized to interface with GUI elementsgenerated on the display device 24. Comparatively, the display device 24can be any image-generating device configured for operation within thecabin 22 of the combine harvester 10. The display device 24 may beaffixed to the static structure of the cabin 22 and realized in ahead-down display (HDD) configuration in embodiments. Additionally, insome embodiments, where agricultural machine 200 operates autonomouslyor semi-autonomously without an operator on board. Operator interface 26may be provided on a remote device such as a cell phone or tabletcomputer, connected to agricultural machine 200.

When included in the RF measurement system 12, the wireless datalink 66may assume the form of an RF transceiver permitting wireless datatransmission and reception with a remotely-located control center ordata source. In various implementations, the datalink 66 can receiveinformation utilized in evaluating crop or soil conditions, weatherconditions, and perhaps in periodically updating or refining RFcharacteristic database 72. Additionally or alternatively, the datalink66 may be utilized to offboard (that is, transmit to a remotely-locatedsource) data gathered by the controller 16, with the remote source thenaggregating the data or other utilizing the data in some manner. Inother embodiments, the datalink 66 may be omitted from the RFmeasurement system 12, as may many of the other components shown in FIG.2 .

Also, the non-RF sensors 64 may include various sensors providing inputdata utilized by the controller 16 in assessing one or more parameterspertaining to the currently-harvested grain processed by the combineharvester 10. Such sensors 64 can include, for example, sensors formeasuring the speed of the clean grain elevator 52 (as useful indetermining mass flow rate) ground speed of harvester 10, and/or sensorsfor detecting harvested material type (useful in filtering the RFcharacteristic testing data 90 to isolate pertinent RF characteristicsin performing the below-described functions to determine mass,contaminant levels, and constituent levels). Additionally, thepossibility that the non-RF sensors 64 may include capacitance sensors,weight sensors, or other such sensors utilized to estimate grainmoisture content is not precluded. When such sensors are present, thedata input provided by the sensors may be utilized to determine moisturecontent independently of or in combination with RF signal responsesignals collected by the RF sensors 54, 56, as further described below.In other instances, moisture content may be determined solely utilizingthe RF signal response signals provided by the RF sensors 54, 56; ordetermined in another manner, such as by operator input received viaoperator interface 26.

Discussing RF sensors 54, 56 in greater detail, the RF sensors 54, 56each include at least one RF emitter 76 and at least one RF receiver 78.As indicated above, the RF sensors 54, 56 are usefully disposed atdifferent locations along the clean grain flow path; although one orboth of the RF sensors 54, 56 can be potentially positioned to captureRF sensor readings of the harvested grain outside of the clean grainflow path in alternative embodiments. In various implementations, and asindicated on the right of FIG. 2 , the RF sensors 54, 56 are integratedinto the structure of the clean grain elevator 52. Specifically, the RFsensor 54 may be strategically positioned to capture RF sensor readingsof the harvested grain when transported upwardly within the clean grainelevator as piles or consolidated masses supported by the grain elevatorpaddles 80 (only a few of which are labeled in FIG. 2 ) projected from aconveyor belt 74 contained in the clean grain elevator 52.Comparatively, the RF sensor 56 may be positioned to capture theharvested grain as the grain is thrown from the paddles 80 and thusdischarged through an outlet 82 of the clean grain elevator 52.Accordingly, in such embodiments, the interrogation area or FOV 84 ofthe RF sensor 56 may be enlarged relative to the interrogation area orFOV 86 of RF sensor 54 to ensure that the RF sensor 56 records thesignal response of substantially all of the airborne grain passedthrough the outlet section 82 of the clean grain elevator 52. This maybe accomplished by tailoring the respective antennae shapes anddimensions of the emitter 76 and receiver 78. In still otherembodiments, the RF sensors 54, 56 may be positioned at the samelocation or essentially the same location within the clean grainelevator 52; e.g., the sensors 54, 56 may be co-located to capture RFsensor readings of the grain when supported by a paddle 80 of the cleangrain elevator 52 or co-located to capture RF sensor readings of thegrain when discharged from the clean grain elevator 52 through outlet82. Collectively, the RF sensors 54, 56 included in the RF grain massand constituent measurement system 12 form an RF sensor subsystem 88.

In embodiments, the RF sensors 54, 56 concurrently capture RF sensorreadings of the currently-harvested grain, while transported along theclean grain flow path. Further, the RF sensor 54 is configured tocapture RF sensor readings of the currently-harvested grain at a firstfrequency or frequency range. Comparatively, RF sensor configured tocapture RF sensor readings of the currently-harvested grain at a secondfrequency or frequency range different than the first frequency orfrequency range. The sensors 54, 56 can operate in either or both of atransmit and a reflected mode. Additionally or alternatively, the RFsensors 54, 56 may each be configured to capture RF sensor readings ofthe grain when subjected to or impinged with RF energy falling withinthe Terahertz band. For example, in such embodiments, a first RF sensormay operate at a first fixed frequency or a maximum frequency (ifemitting RF energy over a frequency range) of f1, while a second sensormay operate at a second fixed frequency or a minimum frequency (ifemitting RF energy over a frequency range) of f2. Further the value off2 may be at least twice that of f1. The frequencies at which the RFsensors 54, 56 operate will vary among embodiments, as will the sensorpositioning; generally, however, the sensor frequencies and positioningare selected to maximize signal-to-noise ratios, avoid structural (e.g.,metallic) interface, and elicit distinct signal responses from the grainto optimize resolution when distinguishing between the RFcharacteristics stored in RF characteristic database 72, as furtherdiscussed below in connection with other FIGS.

The RF sensor readings captured by the RF sensors 54, 56 are providedover wired or wireless data connections to the controller 16. Thecontroller 16 then considers the RF sensor readings provided by the RFsensors 54, 56 in conjunction with data contained within the RFcharacteristic database 72 in assessing unknown parameters pertaining tothe grain processed by the combine harvester 10. Specifically, the RFcharacteristic database 72 contains RF characteristic testing data 90observed for tested grain samples having known properties, while thegrain samples are impinged with RF energy over one or more testedfrequency ranges. When deployed on other harvesters (cotton harvesters,balers, sugarcane harvesters, etc.) then an RF characteristic testingdata 90 will be data observed for other tested samples (of cotton, hay,sugarcane, certain objects, etc.). An “RF signal response” can be any RFsignal measurement captured when impinging RF energy against a harvestmaterial, whether the RF energy is passed through or reflected from thematerial. The RF signal response may be, for example, a measurement of:(i) the attenuation of RF energy when passed through the harvestmaterial (either before or after harvesting); or (ii) the propagationdelay (phase shift) of RF energy when passed through the harvestmaterial. In further implementations, other types of RF signal responsesmay be considered in addition to or in lieu of the attenuation and/orpropagation delay of RF energy impinged against the harvest material. Anon-exhaustive list of such alternative RF signal responses that may beconsidered by the controller 16 includes polarization, power densitydistribution, reflection, absorption, and back scattering. Thecontroller 16 can utilize such RF sensor readings to determine thepresence of objects, the mass, contaminants, and/or one or moreconstituent quantities (e.g., moisture content and/or one or morenon-moisture content percentages) of the harvested material based, atleast in part, on a comparative analysis with the RF characteristictesting data 90 stored in the database 72.

The RF characteristic testing data 90 may be stored as one or more RFsignal response maps 92, 94, 96, as generally indicated in the lowerleft of FIG. 2 . Alternatively, the RF characteristics may be storedutilizing another data structure, such as a multidimensional lookuptable. When stored in one or more response maps 92, 94, 96, the RFcharacteristics may be plotted as traces, lines, or curves on atwo-dimensional graph of frequency range versus measured RF signalresponse parameter. Such traces can be stored as series of discrete,connected points or coordinates; or stored in the form of formulae whenpossible. Examples of such RF signal response maps are discussed morefully below in connection with other FIGS. The RF characteristicdatabase 72 may store a plurality of such maps associated with orcorresponding to different harvested material types, with controller 16then selecting the appropriate response map or maps (e.g., the responsemap 92 shown in the foreground in FIG. 2 ) based upon the type orcategory of harvested material currently processed by the combineharvester 10. Subsequently, the controller 16 may determine or estimatethe harvested material mass, the moisture content, contaminants, and/ora first constituent content of the currently-harvested material based,at least in part, on matching the RF sensor readings with a specific RFsignal response included in the RF signal responses plotted on the RFsignal response map 92. The manner in which the controller 16 mayperform such function will now be described in more detail in connectionwith FIG. 3 .

Referring now to FIG. 3 , an RF measurement process 100 is presented inaccordance with a non-limiting example embodiment. The RF measurementprocess 100 can be carried-out by the controller 16 of the RFmeasurement system 12 in embodiments of the present disclosure. The RFmeasurement process 100 includes a number of process STEPS 102, 104,106, 108, 110, 112, 114, each of which is described, in turn, below.Depending upon the particular manner in which the RF measurement process100 is implemented, each step generically illustrated in FIG. 4 mayentail a single process or multiple sub-processes. Further, the stepsillustrated in FIG. 3 and described below are provided by way ofnon-limiting example only. In alternative embodiments of the RFmeasurement process 100, additional process steps may be performed,certain steps may be omitted, and/or the illustrated process steps maybe performed in alternative sequences.

The RF measurement process 100 is directed to measuring grain mass andconstituents, but this is only one example. Process 100 commences atSTEP 102 in response to the occurrence of a predetermined trigger event.In certain instances, the trigger event may be detection of the intakeof severed crop plants into the combine harvester 10 (FIG. 1 ). In otherinstances, the RF measurement process 100 may be commence in response toa different trigger event, such as in response to operator inputreceived via operator interface 26 indicating that the RF measurementprocess 100 is desirably performed.

After commencing (STEP 102), the RF measurement process 100 advances toSTEP 104. At STEP 104, the controller 16 receives RF sensor readingsfrom RF sensor subsystem 88 (FIG. 2 ). In the illustrated example,specifically, the controller 16 receives RF sensor readings from RFsensors 54, 56 positioned in the clean grain elevator 52 during STEP104. Next (or concurrent with or prior to STEP 104), the controller 16recalls appropriate RF characteristics from the RF characteristicdatabase 72 (FIG. 2 ). As indicated in FIG. 3 by arrow 116, controller16 may determine the pertinent RF characteristics for recollection andsubsequent consideration utilizing various types of filter criteria.Generally, in embodiments, the RF characteristic database 72 may containmultiple datasets of RF characteristics, with each dataset correspondingto a particular type of grain or a particular grain category. In suchembodiments, the controller 16 may identify the particular grain type orgrain category presently processed by the combine harvester 10; e.g.,based upon operator input received via operator interface 26, based onGPS data if correlated to grain type, and/or based on any type ofautomated grain identification technique, such as image processing of alive camera feed or surface response measurements of the harvestedgrain. Examples of grain types include, but are not limited to, corn,canola, soybeans, wheat, oats, and sunflowers. Grain categories may bedifferentiated by general grain compositions, such as protein- oroil-rich grains. The controller 16 may then extract the appropriate RFcharacteristics from the database 72 tagged or linked to thepresently-processed grain type or category. A similar approach can alsobe utilized to filter by moisture content after a moisture content hasbeen estimated by the controller 16, as described below. In otherembodiments, other filter criteria can be utilized; or the controller 16may simply compare all RF characteristics stored in the database 72 tothe RF sensor readings during subsequently-performed STEP 108.

Next, at STEP 108 of process 100 (FIG. 3 ), the controller 16 determinesmultiple unknown parameters describing the currently-processed grainharvested by the combine harvester 10. In various embodiments, suchparameters will include grain mass and the fraction of the harvestedgrain composed of a particular constituent type or types; e.g., protein,cellulose, starch, or oil content. The controller 16 also usefullyestimates moisture content of the harvested grain during or prior toSTEP 108 and then compensates for the moisture content estimate indetermining grain mass and constituent content level(s) for increasedaccuracy. In embodiments, the controller 16 may utilize the RF sensorreadings provided by RF sensors 54, 56 to estimate moisture content;e.g., by comparing the RF sensor readings 54, 56 to the recalled RFcharacteristics for tested grain samples having known moisture contentsof varying levels. Further discussion in this regard is provided belowin connection with FIG. 4 . In other instances, moisture content may bedetermined in another manner; or any such moisture content estimaterendered utilizing the RF sensor data may be blended with other moisturecontent estimates, if available. Generally, then, various types ofnon-RF sensor data input 118 may be considered by the controller 16during STEP 108, as indicated by arrow 118. In instances in which suchdata 118 includes operator input indicative of moisture content, weightor capacitance measurements indicative of moisture content, or othersuch information indicative of moisture content, this data mayalternatively be utilized to determine moisture content or otherwiseconsidered during STEP 108.

The RF sensor readings are compared to the recalled RF characteristicsto estimate grain mass and one or more constituent quantities within theharvested grain. The controller 16 may identify a particularcharacteristic based upon the RF sensor readings to determine unknownparameters (grain mass and grain attribute(s)), noting that the usage ofmultiple RF sensor readings captured at different frequencies orfrequency ranges enables multiple unknown parameters to be discernedutilizing cross-reference techniques. Stated differently, the controller16 may analyze the RF sensor readings utilizing the recalled RFcharacteristic or correlation equations (as established by the testingdata); e.g., top-bottom and in-out measurements can be utilized todevice multiple variables for the constituents in embodiments. Withrespect to grain mass, in particular, the RF sensor readings may beutilized to initially determine a volume of grain as the grain passesthrough a given sensor interrogation area. This may be expressed as, forexample, a grain pile depth in the case of RF sensor 54 shown in FIG. 2, which can then be converted to a volumetric measurement as the widthand length of the grain pile is generally known (determined by theconfigured space between the grain elevator housing 98, the paddles 80,and the conveyor belt 74). The grain volume of each grain pile can thenbe converted to mass (e.g., number of grams) utilizing a knownconversion factor, which may then be converted to grain mass flow rateand crop yield by considering the speed of the clean grain elevator 52(further included in the non-RF sensor inputs 118) and other suchfactors.

After determining grain mass, moisture content, and the constituentcontent(s) of the currently-harvested grain (STEP 108), the controller16 progresses to STEP 110 and performs any number of actions. Suchactions may include any combination of the following: (i) displayingsuch information on the display device 24 for reference by an operator;(ii) storing such information in memory 70 to create, for example, atime-stamped data log for subsequent reference or analysis; (iii)offboarding such information to another entity or system via thedatalink 66; or (iv) commanding actuator(s) 62 to adjust an operatingparameter or component position in response to changes in the grain massflow rate and/or moisture content. Following STEP 110, the controller 16determines whether the RF measurement process 100 should terminate (STEP112) due to, for example, deactivation by an operator or cessation ofcrop harvesting by the combine harvester 10. If it is determined thatthe RF grain mass and constituent measurement process 100 shouldterminate, the controller 16 terminates the process accordingly.Otherwise, the controller 16 returns to STEP 104 and performs a furtheriteration of the RF measurement process 100, as previously described.Such steps may be performed on a relatively rapid basis to allow the RFmeasurement system 12 to measure grain mass and constituent levels(moisture content and/or non-moisture content level(s)) in highlyresponsive, real-time manner.

FIG. 4 presents an example RF response map 120 plotting several RFsignal response characteristics 122, 124, 126, 128, 130, 132 over atested frequency range for a number of tested grain samples.Specifically, in the illustrated example, each of the tested grainsamples corresponding to the RF characteristics 122, 124, 126, 128, 130,132 have a known moisture content of 16%, by weight. In addition to aknown moisture content, the tested grain samples also include known oilcontent levels and pile depths in the illustrated example, as indicatedby a key 134. In the case of RF response map 120, the RF signal responseunder consideration is the propagation delay or phase shift of RF energywhen impinged against (e.g., passed through) the tested grain samples.Various other RF response characteristics for tested grain sampleshaving 16% moisture content, varying oil levels, and/or varying piledepths may also be plotted on the example RF response map 120 inembodiments, but are not shown in FIG. 4 for visual clarity.

Referring to FIGS. 1-3 in combination with FIG. 4 , the controller 16may estimate moisture content of the currently-harvested grain duringSTEP 106 of the RF measurement process 100 (FIG. 3 ) in embodiments.Again, the controller 16 may determine moisture content in any suitablemanner, but usefully does so utilizing multiple correlations establishedby the stored testing data and multiple sensor readings captured by theRF sensors 54, 56. For example, the RF signal response characteristics122, 124, 126, 128, 130, 132 plotted by the RF response map 120 fortested grain sample having an established moisture content level may beconsidered in conjunction with multiple other plotted RF signal responsecharacteristics 122 (or correlation equations) having other establishedmoisture content levels. The current RF sensor readings, as captured fordifferent frequencies or frequency ranges, may then be utilized toidentify the moisture content level by geometric or pattern matching toa particular characteristic or range of candidate characteristic. Thedetermined moisture level may then be utilized to select the RF responsemap 120 for usage in evaluating the pile depth and the oil content levelof the currently-processed grain. For example, in an embodiment in whichan RF sensor reading is captured at a frequency of 8 GHz (as indicatedin FIG. 4 by a vertical line 136), a detected phase shift (unit-less inFIG. 4 , but suitably expressed in degrees) may correspond to a marker138. As the marker 138 falls on or adjacent the characteristic 130, itcan be determined that currently-harvested gran has a pile depth of 2centimeters (cm) and an oil content level of approximately 46% byweight. Once determined, the pile depth can be converted into volume forusage in determining grain mass. A similar approach can also be utilizedto determine the other constituent content levels and/or contaminants ofthe currently-harvested grain, as permitted by the RF sensing readingsand the RF characteristic testing data stored in the database 72.

In the above-described example, a fixed testing frequency of 8 GHz wasdiscussed. Referring further to FIG. 4 , vertical line 140 furtherdenotes a testing frequency of 16 GHz, with marker 142 indicating ahypothetical phase shift value taken along the characteristic or trace130 that may be detected in an alternative practice scenario. Thus, ineither case, the RF sensor readings indicate that thecurrently-harvested grain has a pile depth of 2 cm (as divided into adiscrete pile supported by one of the paddles 80 of the clean grainelevator 52) and an oil content of approximately 46%, by weight.However, as may be appreciate by comparing the vertical spacing betweenthe characteristic 130 and the next closest characteristic 132(identified as “G1” for 8 GHz and “G2” for 16 GHz in FIG. 4 , “G”denoting “gap”), the separation or resolution between characteristicsincreases with increasing frequency. Considering this, there is ageneral benefit to impart the RF sensors 54, 56 with operationalfrequencies or frequency ranges that are higher to enhance resolutionand accuracy. Concurrently, however, the cost and complexity of RFsensors tends to also increase at higher frequencies falling within theRF domain. For these reasons, in at least some applications, the RFsensors 54, 56 each operate at distinct frequencies or frequenciesranges between 1 and 100 GHz in embodiments. In other embodiments,however, one or both of the RF sensors 54, 56 may operate outside of theaforementioned range, providing that sensors 54, 56 operate within theRF domain.

In the example of FIG. 4 , an RF sensor reading captured at a singlefixed RF frequency or fixed RF frequencies is considered. In furtherembodiments, RF sensor 54 and/or RF sensor 56 may capture RF sensorreadings over a predetermined frequency range and, thus, generate an RFresponse signature for the currently-harvested grain. The controller 16may then geometrically match (e.g., utilizing a pattern matching imageanalysis algorithm) the sensor-detected RF signature to a correspondingRF signature or characteristic contained in the RF characteristicdatabase 72. An example of such an RF response characteristic 146 isplotted in an RF response map 144 shown in FIG. 5 . In the map 144,detected changes in RF wave magnitude or amplitude (and thusattenuation) is charted on the vertical axis, while frequency is chartedon the horizontal axis. While the magnitude axis is unit-less in theillustrated example (though the magnitude increase may be logarithmic),decibels or a similar unit may be utilized 16 in actual implementations.Further, in other embodiments, a different RF response (e.g., phaseshift, back scattering, polarization, absorption, reflection, powerdistribution, or a combination thereof) can be charted in a similarmanner. Distinct geometric features that may be utilized for comparativeanalysis include a nadir occurring at a particular minimum magnitude(MMIN) and a corresponding frequency (f1), as identified by marker 148.Additionally, pronounced changes in slope (as indicated by markers 150)on either side of the nadir marker 148 may be considered by location orby spacing in the frequency dimension (as indicated by double-headedarrow 152). Thus, by matching such a sensor reading with a similar, ifnot identical RF characteristic or signature contained within the RFcharacteristic database 72, the controller 16 may identify thecurrently-harvested grain as sharing the same properties (e.g., piledepth, moisture content, contaminant level, and/or consistent contentlevel) as does the tested grain sample corresponding to the identifiedRF characteristic or signature 146.

Through the above-described comparative analysis of the RF sensorreadings with the testing data stored in the RF characteristic database72, mass, contaminant, and constituent measurements can be determined bythe RF measurement system 12 in a highly accurate and responsive manner.Further, such grain parameters can be determined in real-time or nearreal-time, while minimizing calibration requirements through the usageof ground truth data as consolidated into the reference models orcharacteristics stored as RF characteristic testing data. The foregoingprocess steps are presented by way of illustration only and should beconsidered non-limiting, noting that other processing techniques may beemployed in further embodiments enabling attributes (moisture and/ornon-moisture content and contaminant levels) to be determined bycomparative analysis of RF sensor readings to “ground truth” or testingdata stored in an RF characteristic database located onboard the combineharvester or otherwise accessible to the controller 16 of the RFmeasurement system 12.

While the embodiments described above generally provides an RFmeasurement system to obtain grain mass and grain constituentmeasurements, additional embodiments using Terahertz electromagneticradiation can detect additional features of the harvested materialand/or crop. For example, configuring one or both of sensors 54, 65(shown in FIGS. 1 and 2 ) to utilize Terahertz electromagnetic radiationcan provide the ability to detect additional characteristics in theharvested crop, such as the presence of a genetically modified organism(GMO). Further, the placement of an RF sensor in locations of theharvester beyond the clean grain stream can allow additional informationabout the harvested material to be detected. For example, placing aTerahertz-based sensor at any of locations 200, 202, or 204 (shown inFIG. 1 ) allows sensing of additional aspects relative to the harvestingoperation. With a Terahertz-based sensor located at position 200, earlydetection of materials can be accomplished such that the harvester canreact to avoid damaging the front/feeding components. Positioning theTerahertz-based sensor at location 204 can provide additional materialrelative to the clean grain stream, such as the presence of GMO's,pesticide residue, fungus, and/or soil/ash. Finally, positioning theTerahertz-based sensor at location 202 can provide useful informationwhen using an implement or attachments behind the harvester. Forexample, a Terahertz-based sensor at location 202 can detect weed seed.Terahertz-based sensors are particularly effective at sensingnon-ferrous foreign material, which is highly useful in harvesting.

FIG. 6 is a diagrammatic view of a Terahertz-based sensor in accordancewith one embodiment. As shown in FIG. 6 , Terahertz-based sensor 250includes Terahertz source 210 and one or more Terahertz detectors 212,216. Source 210 is disposed to direct Terahertz electromagneticradiation 219 through a detection area 214 to one or more detectors 212,216. In some embodiments, sensor 250 may only detect attenuation ofTerahertz electromagnetic radiation 219 after passing through detectionarea 214, in which case a single detector 212 may be used and positionedto receive the attenuated Terahertz electromagnetic radiation 220. Inother embodiments, sensor 250 may only detect reflection of theTerahertz electromagnetic radiation 219 from material within detectionarea 214, in which case a single detector 216 may be used and positionedto receive the reflected Terahertz electromagnetic radiation 222. Ofcourse, embodiments also include using both such detectors 212, 216 todetect attenuated Terahertz electromagnetic radiation 220 as well asreflected electromagnetic radiation 222. Further, those skilled in theart will appreciate that additional/alternate Terahertz detectors canused to detect other types of interactions, such as backscatter.

Terahertz source 210 can be any suitable device capable of Terahertzelectromagnetic radiation to detection area 214. Examples, of suchsuitable devices include, without limitation, a femtosecond Ti-sapphirelaser, a Yttrium Iron Garnet (YIG)-oscillator, a quantum cascade laser,a P-type germanium laser, a silicon-based laser; a free electron laser,a photoconductive switch, optical rectification, a backward-waveoscillator, a transferred electron device (i.e., Gunn diode), and aresonant tunneling diode. In embodiments where a number of frequencieswithin the Terahertz range (0.1-30 Terahertz) are desired, a variablefrequency source can be used, such as a variable frequency quantumcascade laser. In other embodiments, a plurality of sources 210 can beused with each source 210 having a different band within the Terahertzrange. Additionally, it is expressly contemplated that source 210 mayoperate in a pulsed mode or a continuous wave mode.

Detectors 212, 216 can be any suitable device that can detectelectromagnetic radiation in the Terahertz range. Detectors 212, 216 maybe configured to provide measurement in the frequency domain or the timedomain to controller 16. Examples of detectors 212, 216 include, withoutlimitation, a photoconductive semiconductor, free-space electro-opticsampling using ZnTe and BBO crystals, bolometer, an interferometer,Schottkey diode, backward diode, High-Electron-Mobility-Transistor(HEMT), Golay cell, and a pyroelectric detector.

Terahertz sensor 250 provides a signal that contains significantinformation about the material that the Terahertz electromagneticradiation has passed through and/or reflected from. As shown in FIG. 6 ,the detector(s) 212, 216 are operably coupled to controller 16, whichmay include or be coupled to artificial intelligence engine 218.Utilizing engine 218 allows controller 16 to perform relatively highlevel classifications based on the received signal(s). Engine 218 mayemploy any suitable artificial intelligence and/or machine learningtechniques in the provision of such classification. Examples of suitableartificial intelligence techniques include, without limitation, memorynetworks, Bayes systems, decisions trees, Eigenvectors, Eigenvalues andMachine Learning, Evolutionary and Genetic Algorithms, ExpertSystems/Rules, Support Vector Machines, Engines/Symbolic Reasoning,Generative Adversarial Networks (GANs), Graph Analytics and ML, LinearRegression, Logistic Regression, LSTMs and Recurrent Neural Networks(RNNSs), Convolutional Neural Networks (CNNs), MCMC, Cluster Analysis,Random Forests, Reinforcement Learning or Reward-based machine learning.Artificial intelligence engine 218 may also utilize mathematicaltechniques such as formulas, simultaneous linear equations, statisticalthresholds, ratios, and other techniques. Learning may be supervised orunsupervised.

While a single sensor 250 is shown in FIG. 6 operably coupled tocontroller 16, multiple such sensors located at different positions onthe harvester may be coupled to controller 16 such that AI engine 218receives input from a plurality of Terahertz-based sensors.Additionally, any of the sensors described above may be used to provideadditional inputs to AI engine 218 for enhanced operation andclassification. Further, other types of sensors, such as one or morechemical or chemometric sensors may also be used as additional inputs toAI engine 218.

FIG. 7 is a flowchart of an example process carried out by controller 16to determine one or more parameters of a crop harvest operation relatedto the harvester shown in FIG. 1 . The method begins at block 300 inresponse to the occurrence of a predetermined trigger event. In certaininstances, the trigger event may be detection of the intake of severedcrop plants into the combine harvester 10 (FIG. 1 ). In other instances,the method may commence in response to a different trigger event, suchas in response to operator input received via operator interface 26.

After commencing (block 300), control transfers to block 302, wherecontroller receives RF sensor readings from one or more Terahertz-basedsensors. Additionally, readings may also be received from RF sensorsubsystem 88 (FIG. 2 ). In the illustrated example, specifically, thecontroller 16 receives sensor readings from Terahertz-based sensor 250.At block 304, controller 16 recalls appropriate characteristics from thecharacteristic database 72 (FIG. 2 ). Characteristics can include,without limitation, characteristics of objects to be detected, crop type(such as hay, forage, cotton, sugarcane, grain, etc.), characteristicsof constituents, characteristics of non-grain materials that areconsidered contaminants. The characteristics may comprise absorption,reflectance, backscatter, and other RF properties as a function offrequency. The non-grain materials may comprise metals, plastics, wood,weeds, weed seeds, non-grain plant parts, soil, ash, fungi, biotoxins,genetically modified organisms, or other contaminant. Controller 16 maydetermine the pertinent characteristics for recollection and subsequentconsideration utilizing various types of filter criteria. Generally, inembodiments, the characteristic database 72 may contain multipledatasets of characteristics, with each dataset corresponding to aparticular type of crop or a particular crop category. In suchembodiments, the controller 16 may identify the particular crop type orcrop category presently processed by the combine harvester 10; e.g.,based upon operator input received via operator interface 26, based onGPS data if correlated to crop type, and/or based on any type ofautomated crop identification technique, such as image processing of alive camera feed or surface response measurements of the harvested crop.Examples of grain types include, but are not limited to, corn, canola,soybeans, wheat, oats, and sunflowers. Grain categories may bedifferentiated by general grain compositions, such as protein- oroil-rich grains. The controller 16 may also identify a kernel categoryand/or a kernel type. The controller 16 may then extract the appropriatecharacteristics from the database 72 tagged or linked to thepresently-processed crop type or category. A similar approach can alsobe utilized to filter by moisture content after a moisture content hasbeen estimated by the controller 16, as described below. In otherembodiments, other filter criteria can be utilized; or the controller 16may simply compare all characteristics stored in the database 72 to thesensor readings during a subsequently-performed step.

Next, at block 306, controller 16 determines one or more harvest-relatedparameters relative to the current harvest operation of the combineharvester 10. In various embodiments, such parameters will includeforeign object/material detection, crop characteristics, contaminant(e.g., pesticide, fungus, GMO, soil/ash) presence and/or level.Additionally, controller 16 may also determine one or more of grain (orother harvested material) mass and the fraction of the harvestedmaterial composed of a particular constituent type or types; e.g.,protein, cellulose, sugar, starch, or oil content. In embodiments,controller 16 may utilize the sensor readings provided by one or moreTerahertz-based sensors. Controller 16 may determine the above-notedparameters using a suitable artificial intelligence classifier, such asAI engine 218 (shown in FIG. 6 ). Additionally, various additional typesof sensor data input 118 may be provided to AI engine 218 and consideredby the controller 16 during block 306.

At block 308, controller 16 performs one or more actions based on theone or more parameters determined at block 306. Examples of actionsinclude, without limitation, stopping the forward movement and/or cropprocessing system (e.g., the feeding system) of the harvester, asindicated at block 314. This can allow the operator to pause theharvesting operation and remove any non-crop material, such asnon-ferrous foreign material, from the feeder. Additionally oralternatively, controller 16 may notify the operator of the one or moreparameters, as indicated at block 316. This notification, may includethe display of parameter levels on a display device within the cab ofthe harvester. However, such notification may also include an audible orvisual alarm indicating the determination of one or more parametersabove a certain threshold. As indicated at block 318, another actionthat controller 16 may perform is to the continue running thecompressor/cutting operation to reduce the detected foreign materialsize in order to pass through for the livestock and material processing.As indicated at block 320, controller 16 may command a diverter orpositioner to direct contaminated materials to a different chamber otherthan cutting process, so that the contaminated material can be cleanedup manually/automatically. Controller 16 can control the machine in awide variety of other ways as well, as indicated by block 321. Inaddition to the actions described with respect to blocks 314, 316, 318,320, and 321, controller 16 may take other suitable actions as well orinstead, as indicated at block 322.

After performing one or more actions in response to determining cropcharacteristics and/or contaminants (block 308), controller 16progresses to block 310 and performs any number of actions. Such actionsmay include any combination of the following: (i) storing suchinformation in memory 70 to create, for example, a time-stamped data logfor subsequent reference or analysis; (ii) offboarding such informationto another entity or system via the datalink 66; or (iii) commandingactuator(s) 62 to adjust an operating parameter or component position inresponse to the one or more parameters and/or detection of cropcharacteristics and/or contaminants. At block 310, controller 16 alsodetermines whether the process should terminate due to, for example,deactivation by an operator or cessation of crop harvesting by thecombine harvester 10. If it is determined that the process shouldterminate, controller 16 terminates the process accordingly, asindicated at block 312. Otherwise, controller 16 returns to block 302and performs a further iteration. Such steps may be performed on arelatively rapid basis to allow the system to be highly responsive andallow real-time detection and handling of crop characteristics,contaminants, and/or objects.

While the present discussion has proceeded with respect to a combineharvester 10, it will be appreciated that the sensing and otheroperations can be performed on other agricultural machines as well. Forexample, FIGS. 8 and 9 illustrate an example of a cotton harvester 230.Cotton harvester 230 includes a chassis 239 that is supported by a setof ground engaging elements, illustratively shown as front wheels 232and rear wheels 234, although, in other examples, other types of groundengaging elements are contemplated, such as tracks. An operatorcompartment 237 is supported by the chassis 239 and includes operatorinterface mechanisms 235. A power plant, such as an engine 236, can besupported below the chassis 239. Water, lubricant, and fuel tanks 262may also be supported in and on the chassis 239.

A cotton picker header 261 is coupled to the chassis 239. As cottonharvester 230 moves through a field 231, cotton picker header 261engages cotton plants. The cotton picker header 261 includes a pluralityof cotton picker heads 244 (e.g., cotton picker row units) arrangedside-by-side across the front of the cotton harvester 230. Each cottonpicker head 244 may be identical to the other picker heads 244. Eachpicker head 244 may include a pair of separators 243 laterally spacedapart from one another and forming a channel 245 disposed between them.The channels 245 receive the rows of cotton plants as the cottonharvester 230 is driven through field 231. Each cotton picker head 244includes a respective cotton picking unit 246. Cotton picking units 246remove cotton from the cotton plants.

The cotton harvester 230, as illustrated, includes as a conveyancesystem, an air system 270. Air system 270 can include a crop conveyorcomponent that conveys cotton through the cotton harvester 230, one ormore sensors 292, and a crop conveyer device (e.g., one or more airducts 252 and an air flow generator). The air ducts 252 are coupled toand aligned with header 261 such that the cotton harvested by the header261 can be transported into the cotton harvester 230 through the airducts 252 of the air system 270 powered by air flow (e.g., air flowgenerated by an air flow generator, such as a fan or blower).

The one or more sensors 292 can monitor air flow and/or crop mass flowin the air ducts 252 of the air system 270. In some examples, one ormore sensors 292 can be positioned in the air ducts 252. As an example,cotton harvester 230 may include a plurality of mass flow sensors 292that are mounted across the width of the air ducts 252. In otherexamples, one or more sensors 292 can be positioned adjacent the airducts 252. In the example, illustrated in FIG. 9 , cotton harvester 230may include a plurality of mass flow sensors 292 that are mounted behindthe air ducts 252 with one cotton mass flow sensor 292 mounted per rowunit 244. The air flow, and/or crop mass flow, can be monitored usingvarious types of sensors such as, but not limited to, an HDOC yieldmonitor, a vacuum sensor, an air speed sensor, etc. As an example, theHDOC yield monitor is a microwave-based controller that bounces a signaloff a flowing crop to detect a change in velocity with a slowing cropflow indicative of an air duct 252 being overloaded.

In some examples, a crop receptacle 280 is coupled to the air system270. The crop receptacle 280 can be a module builder 282 having one ormore baler belts 254. In one example, module builder 282 can be used tobuild a module of the crop, such as a bale of cotton 285. In otherexamples, the crop may be ejected by the air duct system 270 into aninternal hopper, and/or ejected from the harvester into an accompanyingholding tank (which may be towed by another vehicle).

The cotton harvester 230 can include an accumulator system 241. Theaccumulator system 241 can include a crop accumulator component thattemporarily stores the harvested crop and one or more sensors 254. Insome examples, the crop accumulator component can comprise anaccumulator 272 and an accumulator capacity monitor. The accumulator 272is configured to receive cotton harvested by the cotton picker header261.

Sensors 254, or feedback devices, can be coupled to the accumulator 272to monitor an accumulator fill level and provide an accumulator filllevel signal indicative of the fill level in the accumulator 272. Insome examples, the accumulator 272 has a low-level sensor 254 a and ahigh-level sensor 254 b. When the high-level sensor 254 b detectsaccumulated crop at the high level, a signal can be provided to controlthe accumulator 272 to empty its crop contents. That is, for example,when the high-level sensor 254 b detects the accumulator fill level isat or exceeds (e.g., rises above) a pre-set sensor threshold level, acontrol signal can be generated to empty the accumulator 272. In someexamples, the high-level sensor 254 b can be configured, such as by itsposition in the accumulator 272, to be triggered before the accumulator272 is completely full. Triggering of the low-level sensor 254 aindicates that the accumulator 272 has released a sufficient amount ofcrop and a control signal can be generated to control the accumulator272 to stop emptying its crop contents. That is, for example, when thelow-level sensor 254 a detects the accumulator fill level is at orexceeds (e.g., drops below) a pre-set sensor threshold level, emptyingof the accumulator 272 can cease. The low-level sensor 254 a can beconfigured, such as by its position in the accumulator 272, to preventemptying of the crop from the accumulator 272 below a desired low level.In one example, sensors 254 are infrared sensors.

In some examples, the accumulator system 241 can include other sensorsto determine an accumulator fill rate and/or fill level. In someexamples, multiple sensors can be mounted at an inlet to the accumulator272 to monitor mass flow rate (e.g., flow rate of the crop through theinlet, or other portions of the conveyor system) and accumulator fillrate. These sensors can measure the mass flow rate and measure the timeto fill the accumulator 272 between the low-level and high-level sensors254 a, 254 b (e.g., accumulator fill rate) to determine yield.

In some examples, it is beneficial to determine the mass in theaccumulator 272 when the fill level is between the low-level andhigh-level sensors 254 a, 254 b. In such examples, sensors can monitorthe mass flow entering and exiting the accumulator 272 (e.g., which canbe based on past accumulator cycles) and incorporate this data withadditional timing data. As an example, a cotton bale diameter can beused to determine a bale growth rate, and the bale growth rate can beused to determine the amount of mass from the size of the modulediameter thereby creating a better estimation of mass in accumulator272.

A feeder 265 is illustratively coupled to the chassis 239. The feeder265 can receive cotton from the accumulator 272. The feeder 265 caninclude a plurality of meter rollers 264 that compress the cotton andtransfer the cotton to the module builder 280 at a feed rate. A firstmotor 255 is positioned to rotate the plurality of meter rollers 264.The first motor 255 may be hydraulic or electric.

A plurality of beater rollers 288 cooperate with the plurality of meterrollers 264 to transfer the cotton to the module builder 280 at the feedrate. A second motor 289 can be positioned to rotate the plurality ofbeater rollers 288. The second motor 289 may be hydraulic or electric.

A feeder belt 286 can receive crop from the plurality of meter rollers264 and beater rollers 288 and transfer the crop to the module builder280 at the feed rate. A third motor 287 is positioned to rotate thefeeder belt 286. The third motor 287 may be, for example, hydraulic orelectric.

As illustrated in FIG. 9 , cotton harvester 230 can also include one ormore THz-based sensors located at one or more different locations oncotton harvester 230. FIG. 9 shows that a first THz-based sensor 250-1can be located at a forward portion of cotton harvester 230, in thedirection of travel, with a field of view oriented to detect items inthe cotton plants, prior to engaging them with header 261. AnotherTHz-based sensor 250-2 can be located in the accumulator 272 so that,after material is engaged by cotton harvester 230 and is transferredthrough air ducts 252, THz-based sensor 250-2 can detect items in theharvested material. FIG. 9 also shows that, in one example, a THz-basedsensor 250-3 can be located in module builder 280 so that, as theharvested material is being formed into a module 285, sensor 250-3 cansense items in the harvested material in module 285. By way of example,THz-base sensor 250-1 can detect objects or constituents or contaminantsor other characteristics or harvest-related parameters of the plantsthat have yet to be engaged by cotton harvester 230. THz-based sensors250-2 and/or 250-3 can sense objects that have entered into cottonharvester 230 and can also detect things such as cellulose in the cottonplants, or other constituents, contaminants, or characteristics (e.g.,moisture, etc.) of the harvested material or other harvest-relatedparameters. The sensors 250-1 through 250-3 can also sense such thingsas fungi, heavy metals, or other chemicals of concern, in the harvestedmaterial or in the material about to be engaged by cotton harvester 230.

As is described elsewhere herein, the output from sensors 250-1 through250-3 can be provided to an AI engine 218 in controller 16 for furtherprocessing. The AI engine 218 can generate an output indicative of thesensed items. For instance, if the sensor signals from sensor 250-1indicate a foreign object that may damage cotton harvester 230, then theAI engine 218 can generate an output indicative of the sensed object andcontroller 16 can generate an output to the operator to stop the cottonharvester 230, to automatically steer around the object, or to performother actions. If an object is detected by sensors 250-2 and/or 250-3,then the harvested material may be diverted so that it is not includedin module 285, or a record can be generated indicating that module 285contains the object or other sensed item or harvest-related parameter(such as fungus, or other chemicals or cellulose), so that appropriateaction can be taken with respect to that module 285. In addition, ageographic position sensor (such as a global navigation satellitesystem—GNSS receiver or other position system) can generate a locationoutput indicative of the geographic location of modules 285 in a localor global coordinate system. Controller 16 can generate a tagcorresponding to module 285 so that the location of the module 285, thatcontains the object or other sensed item, can be identified during laterretrieval of the module 285.

FIG. 9 also shows that a THz-based sensor 250-4 can be carried by anunmanned aerial vehicle (UAV) 290. The UAV 290 can fly, for example,ahead of cotton harvester 230 in the direction of travel of cottonharvester 230 to detect objects or other items with respect to the cropmaterial that has not yet been engaged by cotton harvester 230. It willbe noted that the UAV 290 can have its own controller 16 with AI engine218 to perform processing on the sensor signals from THz-based sensor250-4. The output of AI engine 218 can then be communicated to othersystems. In another example, UAV 290 can have a communication systemwhich communicates the sensor signals from sensor 250-4 back tocontroller 16 and AI system 218 which may reside on the cotton harvester230, or elsewhere.

FIG. 10 shows another example in which the agricultural machine includesa towing vehicle 292 and baler 294. Towing vehicle 292 can be a tractor,or other towing vehicle which can be operated by an operator, or whichcan operate autonomously, or which can operate semi-autonomously. In theexample shown in FIG. 10 , towing vehicle 292 tows baler 294 which,itself, engages severed harvest material, such as alfalfa, grass, orother hay, with a pickup mechanism 296. The harvest material is thenpassed by pickup mechanism 296 into baling chamber 298 which forms abale or module 330. Bale or module 330 can be ejected out the back ofbaler 294, onto the field over which baler 294 is traveling.

In the example shown in FIG. 10 , a THz-based sensor 250-5 can be placedon the towing vehicle 292 and be a forward looking sensor so that itsenses the hay or other material to be baled ahead of towing vehicle292. In this way, sensor 250-5 can detect objects in the harvestmaterial or characteristics of the harvest material (such as moisture,the presence of chemicals or fungi or mold, the nutrient contents orother constituents or contaminants in the material, etc.).

Similarly, a THz-based sensor 250-6 can be located on baler 294proximate intake mechanism 296. Sensor 250-6 can thus sense items in theharvest material either before or after it is picked up by pickupmechanism 296 and/or as the material is moving into the baling chamber298. Sensor 250-6 can thus detect objects in the harvest material and/orcharacteristics of the harvest material either just prior to the harvestmaterial being picked up pickup mechanism 296 or prior to the harvestmaterial entering the baling chamber 298. It will be appreciated thatthe THz-based sensor 250 can be located elsewhere on towing vehicle 292or baler 294 as well, and the locations of sensors 250-5 and 250-6 areshown for the sake of example only. FIG. 10 also shows that, in oneexample, a UAV 290 can be used with a sensor 250-4. UAV 290 can then flyahead of towing vehicle 292, in the direction of travel, to sense itemsin or near the harvest material before that material is engaged bypickup mechanism 296 of baler 294.

As with other examples, the sensor signal generated by any of thesensors 250-4, 250-5, and/or 250-6 can be provided to controller 16 andAI component 218. AI component 218 can identify objects orcharacteristics of the material (e.g., constituents, contaminants,and/or moisture content, etc.) that is sensed, based upon the sensorsignals. The controller 16 can perform a variety of different actionsbased upon what is sensed. For instance, a controller can generate anoperator interface signal that can be used to generate an operatorinterface for display to an operator of towing vehicle 292. Controller16 can automatically steer the towing vehicle 292 or control thepropulsion system of towing vehicle 292 (such as to bring the towingvehicle 292 and/or the baling system in baler 294 to a stop). Thecontroller 16 can generate an output indicating that an object is in themodule 330 being formed, as well as the location of that module.Controller 16 can generate an output indicative of the characteristicsof the material that is being used to form bale or module 330 andcorrelate those characteristics to the particular bale or module 330 sothat the bale or module 330 can be used appropriately (such as fed tocertain animals but not others, fed earlier in the year or later, orused for another purpose), based upon the characteristics of thematerial (constituents, contamination, moisture content, etc.) that wasused to form that particular module 330. Of course, the controller 16can perform a wide variety of other control actions based upon the itemsidentified by AI engine 218, given the sensor signals from the THz-basedsensors. FIG. 11 illustrates one example in which the agriculturalharvesting machine is a sugarcane harvester 350. As shown in FIG. 11 ,harvester 350 includes a cab 352 for an operator and a frame 354 thatsupports various cutting, routing, and processing devices. Frame 354 issupported by a transport frame, such as a track frame supporting trackassemblies 356. In another example, harvester 350 can include wheelssupported by axel assemblies.

An engine 358 powers a main hydraulic pump (not shown) and variousdriven components of harvester 350 can be powered by hydraulic motors(not shown) receiving hydraulic power from the main hydraulic pump.

A cane topper 359 extends forward of frame 354 and is configured toremove the leafy tops of sugarcane plants (not shown). A set of cropdividers (e.g., left-side divider 360 shown in FIG. 11 ) is configuredto guide the remainder of the sugarcane toward internal mechanisms ofharvester 350 for processing. As harvester 350 moves across the field,plants passing between the crop dividers 360 are deflected downward by aknockdown roller 362 before being cut near the base of the plants by oneor more basecutters (or other cutting devices) 364.

Rotating disks, guides, paddles (not shown in FIG. 11 ) or othertransport devices on basecutter(s) 364 are configured to direct the cutends of the plants upwardly and rearwardly within harvester 350 toward afeed train 366, which can include successive pairs of upper and lowerfeed rollers 368 and 370. A set of intake rollers 372 and 374 areconfigured to receive cut sugarcane from basecutters 364 at the frontend of feed train 366. Feed rollers 368 and 370 are rotated to conveythe received sugarcane toward chopper drums 376 and 378 for choppinginto relatively uniform billets. The sugarcane can be cleaned by aprimary extractor 380 and carried up a loading elevator 382 fordischarge into a trailing truck or other receptacle (not shown in FIG.11 ).

Harvester 350 can include a plurality of field sensors configured toobtain indications of field topography in unharvested areas in the pathof harvester 350 (Some examples of such sensors can include, but are notlimited to, RADAR detection systems, ultrasonic sensors, cameras orother imaging sensors, etc.

Harvester 350 may also include a plurality of post-harvest in-situ fieldsensors configured to receive in-situ data from the areas of field afterthose areas have been harvested. The post-harvest in-situ field sensorscan be configured to measure furrow depth between adjacent crop beds.“Crop bed” refers to the planting locations of the crop plants(generally in rows), and “furrow” refers to the area between adjacentcrop beds.

Harvester 350 can also include in-situ field sensors that acquire datarepresenting the area of crop rows that have already been harvested. Forexample, such sensors can generate indications of the height of the cropbed of crop rows, after the crop has been harvested from that area offield. Alternatively, or in addition, such sensors can generateindications of stubble height remaining after the crop has been cut byharvester 350.

These are examples of some types of sensors that can be used onsugarcane harvester 350 and additional or different sensors can be usedas well.

Some current sensors on sugarcane harvester 350 have difficulty indetecting foreign objects, especially smaller stones. For instance,small stones can become stuck or otherwise lodged in the sugarcane stemswhile harvesting. Those stones are thus passed to the other componentsin sugarcane harvester 350 because they are not detected. The same istrue for other objects that are not detected, regardless of size.

FIG. 11 also shows that sugarcane harvester 350 may be equipped with oneor more THz-based sensors 250. For example, a THz-based sensor 250-7 maybe mounted in a forward looking posture on the front of sugarcaneharvester 350, such as on the front of cab 352. Thus, sensor 250-7 cansense objects or other characteristics of the sugarcane (harvest-relatedparameters) ahead of harvester 350.

FIG. 11 also shows that a THz-based sensor can be disposed to sense thematerial just prior to, or just after, it is cut by base cutters 364.Sensor 250-8 can therefore detect objects or characteristics of the cropbefore they are engaged by based cutter 364 or objects orcharacteristics of the severed material, after it is cut by base cutter364 but before it is moved further into sugarcane 350 by intake rollers372 and, prior to reaching chopper drums and 378. A THz-based sensor250-9 can also be located to sense material in extractor 380, or as thematerial travels along loading elevator 382. The THz-based sensor canalso be located on the loading elevator 382 to sense material beingunloaded into a billet cart. One such sensor is illustrated as sensor250-10.

It will also be noted that a UAV can be deployed in the sugarcane fieldand equipped with a THz-based sensor 250. The UAV can fly forward ofsugarcane harvester 350 in the direction of travel of harvester 350 tosense objects and/or crop characteristics in the sugarcane field aswell.

The outputs from the one or more THz-based sensor 250 in FIG. 11 canalso be provided to a controller 16 with an AI engine 218 (or anothertype of processor) to identify objects or characteristics of theharvested material or in the field based on the sensor signals. Where anobject is detected in the sugarcane, the controller 16 can generate anoutput to the operator of sugarcane harvester 350, an automated outputto stop sugarcane harvester 350 or automatically steer sugarcaneharvester 350 around the object, or generate other output signals.Similarly, where an object is detected within a sugarcane stem, forinstance, that portion of harvested material can be diverted from theremaining harvesting path in sugarcane harvester 350. The THz-basedsensors can also be used to sense characteristics of the sugarcaneeither before or after it is harvested. For instance, the sensors 250can be used to measure the sugar content of the billets, and/or todetect toxins or other material on the billets (such as fungi, heavymetals, other chemicals of concern, etc.). When such characteristics aresensed, the controller can also generate a variety of different types ofoutput signals which may alert the operator, identify that portion ofthe harvested sugarcane as being suitable for some uses but not forother uses (depending, for instance, on the sugar content, the type ofchemicals or other constituents found on the sugarcane, etc.)

FIG. 12 is a block diagram showing one example of controller 16, in moredetail. Some items in controller 16 are similar to those described aboveand they are similarly numbered. In the example shown in FIG. 12 ,controller 16 can include one or more processors or servers 432, datastore 434, machine speed detector/estimator 436, machine settingsdetector 438, deceleration profile generator 440, subsequent operationidentification system 442, control signal generator 444, operationresumption system 446, and other items 448. Machine speeddetector/estimator 436 can detect or estimate the ground speed of theagricultural harvester. Machine settings detector 438 can detect any ofa wide variety of other machine settings, some of which are describedelsewhere herein. Deceleration profile generator 440 generates adeceleration profile. For example, when an object is detected ahead ofthe agricultural harvester. The deceleration profile may indicate howquickly to decelerate the harvester and may be based upon the currentground speed of the harvester as well as the distance in front of theagricultural machine that the object is detected. Subsequent operationidentification system 442 can identify a subsequent operation to beperformed based upon the object or other items detected. For instance,if an object is detected ahead of the agricultural machine in thedirection of travel, then the subsequent operation may be to remove theobject from the path of the harvester. Similarly, if an object has beendetected inside the harvester, then the subsequent operation may also beto remove the object from within the harvester. If a contaminant orobject is detected in a bale, for instance, then the subsequentoperation may be to eject the bale or to mark the bale as containing theobject or contaminant. These are just examples of subsequent operationsand others can be identified as well. Control signal generator 444generates control signals to control the agricultural machine to performthe identified subsequent operation and to perform other controlledoperations as well. For example, control signal generator 444 maygenerate control signals to control the propulsion system of the towingvehicle of a baler to decelerate and stop that vehicle when an object isdetected. Operation resumption system 446 generates control signals toresume the prior operation, such as to accelerate the baler to theprevious ground speed, to resume the previous machine settings, and tocontinue the processing operation (e.g., the baling operation) as it wasbeing performed prior to stopping.

FIG. 13 is a flow diagram illustrating one example of determining cropcharacteristics, contaminants, objects, etc. (harvest-relatedparameters), based upon the recalled RF/THz sensor characteristics(described above with respect to block 306 in FIG. 7 ). The processingdiscussed with respect to FIG. 13 can be performed by controller 16 andAI engine 218, or by other systems. The processing will be describedwith respect to controller 16 and AI engine 218, but this is for thesake of example only.

AI engine 218 first receives an indication of the sensor signals fromthe various sensors described above, including the THz-based sensorsignals from THz-based sensors 250. Receiving an indication of thesensor signals is indicated by block 390 in the flow diagram of FIG. 13. The sensor signals can be from the THz-based sensors or other sensors,as indicated by block 392. The THz-based sensors can be located on aUAV, as indicated by block 394, on a towing vehicle as indicated byblock 396, on a crop-processing machine (e.g., the combine harvester,baler, cotton harvester, sugarcane harvester, etc.) as indicated byblock 398, or at other locations as indicated by block 400.

AI engine 218 may be a classifier implemented by a deep neural networkor other artificial intelligence or machine learning system (some ofwhich are mentioned elsewhere herein) that is trained to performclassification or other detection based upon the sensor signal inputsand the recalled RF/THz characteristics. Therefore, thosecharacteristics and the sensor signals (or an indication of the sensorsignals) are applied to AI engine 218 which performs a classification orother detection, as indicated by block 402 in the flow diagram of FIG.13 . The classification or detection can be performed based uponabsorption, reflection, and/or transmission or other characteristicsrepresented by the sensor signals, as indicated by block 404. Theclassification or detection can be performed based on a spectralresponse at one or more different frequencies, as indicated by block406, or the classification or detection can be performed in other ways,as indicated by block 408.

Controller 16 then generates an output based upon the classification ordetection performed by AI engine 218. Generating the output is indicatedby block 410 in the flow diagram of FIG. 13 . The classification can,for example, indicate that an object (e.g., metal, plastic, or otherobject) has been detected by one of the THz-based sensors, as indicatedby block 412. The output can identify any toxic or other contaminants(such as chemical pesticides, biotoxins, fungi, mold, GMOs, heavymetals, hydraulic fluid, toxic weeds, or other contaminants), asindicated by block 414 in the flow diagram of FIG. 13 . The output canidentify constituents of the harvested material, such as sugar contentin sugarcane, fiber cellulose or seed oil levels in cotton, protein inhay, or any of a wide variety of other constituents, some of which aredescribed elsewhere herein, as indicated by block 416 in the flowdiagram of FIG. 13 . The output can represent a wide variety of otherdetected items indicated by the classification or detection output, asindicated by block 418 in the flow diagram of FIG. 13 .

FIGS. 14A and 14 B (collectively referred to herein as FIG. 14 ) show aflow diagram illustrating one example of control actions that are takenin response to detecting a foreign object or other constituents orcontaminants (harvest-related parameter) in the material to be baled orotherwise processed. It is first assumed that a detection is made of aforeign object or contaminants, as indicated by block 420 in the flowdiagram of FIG. 14 . One example of detecting the object or contaminantsis discussed above with respect to FIG. 13 and elsewhere herein. In oneexample, an object is detected ahead of the harvester in the directionof travel, as indicated by block 422, and a distance that the object isdetected from the harvester is also identified, as indicated by block424. An object or contaminants can be detected in other ways, and atother locations as well, as indicated by block 426.

Machine speed detector/estimator 436 then either detects or estimatesthe ground speed at which the harvester is traveling, at block 428. Forinstance, where a ground speed sensor is deployed on the harvester, thenthe ground speed sensor output can be used to detect the ground speed ofthe harvester. However, the ground speed can also be estimated usingother sensors, such as RADAR sensors, geographic position sensors, orother sensors on the harvester or on a towing vehicle. Machine settingsdetector 438 can detect any of a wide variety of other machine settingsand save those settings as well, as indicated by block 430. Forinstance, the engine speed can be detected, the power takeoff speed canbe detected, and/or any of a wide variety of other machine settings canbe detected and saved as well.

Deceleration profile generator 440 then generates a deceleration signal,which may be a deceleration profile which identifies how the harvesteris to be decelerated, or another deceleration signal, as indicated byblock 450 in the flow diagram of FIG. 14 . Control signal generator 444then generates control signals to stop the harvester, as indicated byblock 452. The control signal generator 444 can generate the controlsignals to control the propulsion system of the harvester to stop themachine based upon the deceleration signal (e.g., based on thedeceleration profile or based on another deceleration signal), asindicated by block 454. The control signals can automatically controlthe propulsion system of the harvester to stop the machine as indicatedby block 456, and/or an alert can be displayed for the operator tomanually stop the harvester or to authorize the automated vehiclestoppage, as indicated by block 458. By automatically it is meant, forexample, that the operation or function is performed without furthermanual involvement except, perhaps, to initiate or authorize theoperation or function. The control signal generator 444 can generateother control signals as well, as indicated by block 460 in the flowdiagram of FIG. 14 .

Control signal generator 444 can also generate a control signal tocontrol the transmission of the towing vehicle or self-propelledharvester to place the transmission in neutral, as indicated by block462 in the flow diagram of FIG. 14 .

At some point, subsequent operation identification system 442 canidentify an operation to perform (instead of or in addition to stoppingthe harvester) based on the detected harvest-related parameter. Thesubsequent operation can then be performed either automatically,manually, or semi-automatically, as indicated by block 464. Somesubsequent operations can be, for instance, to stop the materialprocessing machine (e.g., the baler, the accumulator or module builderin cotton harvester, the sugarcane harvester, etc.), as indicated byblock 466. Another subsequent operation can include removing objectsfrom in front of the machine or from within the machine, as indicated byblock 468. Another operation can be to control the machine to divert thematerial from further processing (such as to divert sugarcane billetscontaining objects from being loaded into the billet wagon, to diverthay or cotton that contain objects or other contaminants from beingincluded in a bale, etc.). Diverting the material from furtherprocessing is indicated by block 470 in the flow diagram of FIG. 14 . Inanother example, where the object or contaminants that were detected arealready included in a bale or module, the subsequent operation may be tocontrol the baler or cotton harvester to eject the bale or modulecontaining the object or contaminants, as indicated by block 472, sothat the processing system can begin forming a new bale or module. Inanother example, the subsequent operation may be to generate a bale ormodule marker (such as an RFID tag or an associated data record) markingthe bale or module to indicate that the bale or module contains anobject or contaminants or other detected items. Generating the bale ormodule marker is indicated by block 474 in the flow diagram of FIG. 14 .It will be noted, of course, that the subsequent operation can take awide variety of other forms, as indicated by block 476 in the flowdiagram of FIG. 14 .

Controller 16 can then determine whether the subsequent operation hasbeen completed, as indicated by block 478. For instance, controller 216can receive an input from a sensor detecting when an object has beenremoved, when the bale or module marker has been generated, or when anyof a wide variety of other subsequent operations have been performed. Inanother example, controller 16 can receive an operator input indicatingthat the subsequent operation has been performed. Until the subsequentoperation has been completed, processing reverts to block 464. However,once the subsequent operation has been completed, then operationresumption system 446 can recall the machine settings that existed priorto stopping the machine to perform the subsequent operation. Recallingthe machine settings is indicated by block 480 in the flow diagram ofFIG. 14 . Operation resumption system 446 can then generate anacceleration profile which can be used to accelerate the machine to itsprior ground speed. Generating the acceleration profile is indicated byblock 482 in the flow diagram of FIG. 14 . Operation resumption system446 can then use control signal generator 444 to generate controlsignals to resume the material processing/harvesting operation, asindicated by block 484.

Operation resumption system 446 can use control signal generator 444 togenerate control signals to start the baler or other processing system,as indicated by block 486. Operation resumption system 446 can generatesignals to accelerate the machine to its previous ground speed, asindicated by block 488. Operation resumption system 446 can alsogenerate signals to resume operating with the prior machine settings, asindicated by block 490 and to resume the material processing/harvestingoperations in other ways as well, as indicated by block 492.

It will be noted that the above discussion has described a variety ofdifferent systems, components, detectors, generators, estimators, and/orlogic. It will be appreciated that such systems, components, detectors,generators, estimators, and/or logic can be comprised of hardware items(such as processors and associated memory, or other processingcomponents, some of which are described below) that perform thefunctions associated with those systems, components, detectors,generators, estimators, and/or logic. In addition, the systems,components and/or logic can be comprised of software that is loaded intoa memory and is subsequently executed by a processor or server, or othercomputing component, as described below. The systems, components,detectors, generators, estimators, and/or logic can also be comprised ofdifferent combinations of hardware, software, firmware, etc., someexamples of which are described below. These are only some examples ofdifferent structures that can be used to form the systems, components,detectors, generators, estimators, and/or logic described above. Otherstructures can be used as well.

The present discussion has mentioned processors, processing systems,controllers and/or servers. In one example, these can include computerprocessors with associated memory and timing circuitry, not separatelyshown. They are functional parts of the systems or devices to which theybelong and are activated by, and facilitate the functionality of theother components or items in those systems.

Also, a number of user interface (UI) displays have been discussed. The(UI) displays can take a wide variety of different forms and can have awide variety of different user actuatable input mechanisms disposedthereon. For instance, the user actuatable input mechanisms can be textboxes, check boxes, icons, links, drop-down menus, search boxes, etc.The mechanisms can also be actuated in a wide variety of different ways.For instance, the mechanisms can be actuated using a point and clickdevice (such as a track ball or mouse). The mechanisms can be actuatedusing hardware buttons, switches, a joystick or keyboard, thumb switchesor thumb pads, etc. The mechanisms can also be actuated using a virtualkeyboard or other virtual actuators. In addition, where the screen onwhich the mechanisms are displayed is a touch sensitive screen, themechanisms can be actuated using touch gestures. Also, where the devicethat displays them has speech recognition components, the mechanisms canbe actuated using speech commands.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. All can belocal to the systems accessing them, all can be remote, or some can belocal while others are remote. All of these configurations arecontemplated herein.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used so thefunctionality is performed by fewer components. Also, more blocks can beused with the functionality distributed among more components.

FIG. 15 is a block diagram of one example of the agricultural machinearchitecture, shown in FIG. 1 , where agricultural machine 10, 230, 291,350, communicates with elements in a remote server architecture 2. In anexample, remote server architecture 2 can provide computation, software,data access, and storage services that do not require end-user knowledgeof the physical location or configuration of the system that deliversthe services. In various examples, remote servers can deliver theservices over a wide area network, such as the internet, usingappropriate protocols. For instance, remote servers can deliverapplications over a wide area network and they can be accessed through aweb browser or any other computing component. Software or componentsshown in FIGS. 2, 6 , and/or 12 as well as the corresponding data, canbe stored on servers at a remote location. The computing resources in aremote server environment can be consolidated at a remote data centerlocation or they can be dispersed. Remote server infrastructures candeliver services through shared data centers, even though they appear asa single point of access for the user. Thus, the components andfunctions described herein can be provided from a remote server at aremote location using a remote server architecture. Alternatively, theycan be provided from a conventional server, or they can be installed onclient devices directly, or in other ways.

In the example shown in FIG. 15 , some items are similar to those shownin other FIGS. and they are similarly numbered. FIG. 15 specificallyshows that data store 70, 434 and parts of all controller 16, and/or AIengine 218 can be located at a remote server location 4. Therefore,agricultural machine 10, 230, 291, 350 accesses those systems throughremote server location (e.g., cloud) 4.

FIG. 15 also depicts another example of a remote server architecture.FIG. 8 shows that it is also contemplated that some elements of previousFIGS. are disposed at remote server location 4 while others are not. Byway of example, data store 70, 434 can be disposed at a locationseparate from location 4, and accessed through the remote server atlocation 4. In some examples, agricultural machine 10, 230, 291, 350operates autonomously or semi-autonomously without operator 258 onboard. User 254 may monitor control operation of agricultural machine10, 230, 291, 350 using remote system 251, such as a cell phone ortablet computer, connected to agricultural machine 10, 230, 291, 350 viaremote server location 4. Other systems 238 can also be located in cloud4 or elsewhere and accessed by other items. Other systems 238 caninclude farm manager systems, vendor systems, manufacturer systems, etc.Also, the items can communicate with one another using a wide areanetwork, a local area network, cellular communication, a controller areanetwork (CAN) bus and bus controller, a Wi-Fi or Bluetooth system, anear field communication system or any of a wide variety ofcommunication systems or combination of systems.

Regardless of where they are located, they can be accessed directly byagricultural machine 10, 230, 291, 350, through a network (either a widearea network or a local area network), they can be hosted at a remotesite by a service, or they can be provided as a service, or accessed bya connection service that resides in a remote location. Also, the datacan be stored in substantially any location and intermittently accessedby, or forwarded to, interested parties. For instance, physical carrierscan be used instead of, or in addition to, electromagnetic wavecarriers. In such an example, where cell coverage is poor ornonexistent, another mobile machine (such as a fuel truck) can have anautomated information collection system. As the agricultural machinecomes close to the fuel truck for fueling, the system automaticallycollects the information from the machine or transfers information tothe machine using any type of ad-hoc wireless connection. The collectedinformation can then be forwarded to the main network as the fuel truckreaches a location where there is cellular coverage (or other wirelesscoverage). For instance, the fuel truck may enter a covered locationwhen traveling to fuel other machines or when at a main fuel storagelocation. All of these architectures are contemplated herein. Further,the information can be stored on the agricultural machine until theagricultural machine enters a covered location. The agriculturalmachine, itself, can then send and receive the information to/from themain network.

It will also be noted that the elements of previous FIGS., or portionsof them, can be disposed on a wide variety of different devices. Some ofthose devices include servers, desktop computers, laptop computers,tablet computers, or other mobile devices, such as palm top computers,cell phones, smart phones, multimedia players, personal digitalassistants, etc.

FIG. 16 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's handheld device 16, in which the present system (or parts ofthe present system) can be deployed. For instance, a mobile device canbe deployed in the operator compartment of the harvester for use ingenerating, processing, or displaying harvest-related parameters. FIGS.17-18 are examples of handheld or mobile devices.

FIG. 16 provides a general block diagram of the components of a clientdevice 616 that can run some components shown in other FIGS., thatinteracts with them, or both. In the device 616, a communications link13 is provided that allows the handheld device to communicate with othercomputing devices and under some embodiments provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one embodiment, are provided to facilitate inputand output operations. I/O components 23 for various embodiments of thedevice 16 can include input components such as buttons, touch sensors,optical sensors, microphones, touch screens, proximity sensors,accelerometers, orientation sensors and output components such as adisplay device, a speaker, and or a printer port. Other I/O components23 can be used as well.

Clock 25 illustratively includes a real time clock component thatoutputs a time and date. Clock 25 can also, illustratively, providetiming functions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographic location of device 616. Location system 27 caninclude, for instance, a global positioning system (GPS) receiver, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 can also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 can be activated by other components tofacilitate their functionality as well.

FIG. 17 shows one example in which device 616 is a tablet computer 850.In FIG. 17 , computer 850 is shown with user interface display screen852. Screen 852 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Screen 852 can also use anon-screen virtual keyboard. Of course, screen 852 might also be attachedto a keyboard or other user input device through a suitable attachmentmechanism, such as a wireless link or USB port, for instance. Computer850 can also illustratively receive voice inputs as well.

FIG. 18 shows that the device can be a smart phone 71. Smart phone 71has a touch sensitive display 73 that displays icons or tiles or otheruser input mechanisms 75. Mechanisms 75 can be used by a user to runapplications, make calls, perform data transfer operations, etc. Ingeneral, smart phone 71 is built on a mobile operating system and offersmore advanced computing capability and connectivity than a featurephone.

Note that other forms of the devices 16 are possible.

FIG. 19 is one example of a computing environment in which elements ofprevious FIGS., or parts of them, (for example) can be deployed. Withreference to FIG. 19 , an example system for implementing someembodiments includes a computing device in the form of a computer 1010.Components of computer 1010 may include, but are not limited to, aprocessing unit 1020 (which can comprise processors or servers fromprevious FIGS.), a system memory 1030, and a system bus 1021 thatcouples various system components including the system memory to theprocessing unit 1020. The system bus 1021 may be any of several types ofbus structures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Memoryand programs described with respect to previous FIG. can be deployed incorresponding portions of FIG. 19 .

Computer 1010 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 1010 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerstorage media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 1010. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 1030 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 1031and random access memory (RAM) 1032. A basic input/output system 1033(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 1010, such as during start-up, istypically stored in ROM 1031. RAM 1032 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 1020. By way of example, and notlimitation, FIG. 19 illustrates operating system 1034, applicationprograms 1035, other program modules 1036, and program data 1037.

The computer 1010 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 19 illustrates a hard disk drive 1041 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 1055,and nonvolatile optical disk 1056. The hard disk drive 1041 is typicallyconnected to the system bus 1021 through a non-removable memoryinterface such as interface 1040, and optical disk drive 1055 istypically connected to the system bus 1021 by a removable memoryinterface, such as interface 1050.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASIC s),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 19 , provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 1010. In FIG. 19 , for example, hard disk drive 1041 isillustrated as storing operating system 1044, application programs 1045,other program modules 1046, and program data 1047. Note that thesecomponents can either be the same as or different from operating system1034, application programs 1035, other program modules 1036, and programdata 1037.

A user may enter commands and information into the computer 1010 throughinput devices such as a keyboard 1062, a microphone 1063, and a pointingdevice 1061, such as a mouse, trackball or touch pad. Other inputdevices (not shown) may include a joystick, game pad, satellite dish,scanner, or the like. These and other input devices are often connectedto the processing unit 1020 through a user input interface 1060 that iscoupled to the system bus, but may be connected by other interface andbus structures. A visual display 1091 or other type of display device isalso connected to the system bus 1021 via an interface, such as a videointerface 1090. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 1097 and printer 1096,which may be connected through an output peripheral interface 1095.

The computer 1010 is operated in a networked environment using logicalconnections (such as a local area network—LAN, or wide area network—WAN,or a controller area network—CAN) to one or more remote computers, suchas a remote computer 1080.

When used in a LAN networking environment, the computer 1010 isconnected to the LAN 1071 through a network interface or adapter 1070.When used in a WAN networking environment, the computer 1010 typicallyincludes a modem 1072 or other means for establishing communicationsover the WAN 1073, such as the Internet. In a networked environment,program modules may be stored in a remote memory storage device. FIG. 19illustrates, for example, that remote application programs 1085 canreside on remote computer 1080.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Example 1 is an agricultural system, comprising:

an agricultural harvester;

a terahertz sensor including:

a terahertz source disposed to direct electromagnetic radiation toward aharvest material;

a terahertz detector disposed to detect the terahertz electromagneticradiation after the terahertz electromagnetic radiation interacts withthe harvest material; and

a controller operably coupled to the terahertz detector, the controllerbeing configured to detect a harvest-related parameter based on a signalfrom the terahertz detector and to perform an action based on thedetected parameter.

Example 2 is the agricultural system of any or all previous exampleswherein the agricultural harvester comprises:

a baler configured to gather the harvest material from a field andprocess the gathered harvest material to generate a bale of harvestmaterial.

Example 3 is the agricultural system of any or all previous exampleswherein the controller comprises:

a control signal generator configured to generate a control signal toconfigure a data record corresponding to the bale, the data recordindicating that the bale includes the detected parameter.

Example 4 is the agricultural system of any or all previous exampleswherein the control signal generator is configured to generate a controlsignal to configure a radio frequency identification tag correspondingto the bale and indicating that the bale includes the detectedparameter.

Example 5 is the agricultural system of any or all previous exampleswherein the baler comprises:

a cotton harvester configured to process the gathered harvest materialto generate, as the bale, a module of harvested cotton.

Example 6 is the agricultural system of any or all previous exampleswherein the controller is configured to detect, as the detectedparameter, a parameter indicative of cellulose in the module ofharvested cotton.

Example 7 is the agricultural system of any or all previous exampleswherein the controller comprises:

a control signal generator configured to generate a control signal tocontrol the baler to eject the bale based on the detected parameter.

Example 8 is the agricultural system of any or all previous exampleswherein the controller comprises:

a machine speed detector configured to detect a ground speed of theagricultural harvester; and

a deceleration profile generator configured to generate a decelerationprofile based on the detected parameter and the ground speed of theagricultural harvester; and

a control signal generator configured to generate a control signal tostop the agricultural harvester based on the deceleration profile.

Example 9 is the agricultural system of any or all previous exampleswherein the controller comprises:

a machine settings detector configured to detect a machine setting valueon the agricultural harvester prior to stopping the agriculturalharvester; and

a subsequent operation identification system configured to identify asubsequent operation to perform based on the detected parameter.

Example 10 is the agricultural system of any or all previous exampleswherein the controller comprises:

an operation resumption system configured to resume operation of theagricultural machine after the subsequent operation is performed, basedon the machine setting value and the detected ground speed of theagricultural harvester prior to controlling the agricultural harvesterbased on the deceleration profile.

Example 11 is the agricultural system of any or all previous exampleswherein the agricultural harvester comprises:

a sugarcane harvester configured to sever sugarcane and cut the severedsugarcane into billets, wherein the terahertz sensor is configured todetect the harvest-related parameter in the sugarcane.

Example 12 is the agricultural system of any or all previous exampleswherein the controller is configured to detect, as the detectedparameter, a parameter indicative of an object in at least one of thesugarcane billets.

Example 13 is the agricultural system of any or all previous exampleswherein the controller is configured to detect, as the detectedparameter, a parameter indicative of sugar content in the sugarcanebillets.

Example 14 is the agricultural system of any or all previous examplesand further comprising:

an unmanned aerial vehicle (UAV) configured to travel head of theagricultural harvester, the terahertz sensor being mounted to the UAV todetect the harvest-related parameter in the harvest material ahead ofthe agricultural harvester.

Example 15 is an agricultural system, comprising:

an agricultural harvester configured to process harvest material togenerate a bale or module of the harvest material;

a terahertz sensor including:

a terahertz source disposed to direct electromagnetic radiation towardthe harvest material;

a terahertz detector disposed to detect the terahertz electromagneticradiation after the terahertz electromagnetic radiation interacts withthe harvest material; and

a controller operably coupled to the terahertz detector, the controllerbeing configured to detect a harvest-related parameter based on a signalfrom the terahertz detector and to perform an action based on thedetected parameter.

Example 16 is the agricultural system of any or all previous exampleswherein the controller comprises:

a machine speed detector configured to detect a ground speed of theagricultural harvester; and

a deceleration profile generator configured to generate a decelerationprofile based on the detected parameter and the ground speed of theagricultural harvester; and

a control signal generator configured to generate a control signal tostop the agricultural harvester based on the deceleration profile.

Example 17 is the agricultural system of any or all previous exampleswherein the controller comprises:

a control signal generator configured to generate a control signal toconfigure a data record corresponding to the bale or module, the datarecord indicating that the bale or module includes the detectedparameter.

Example 18 is an agricultural system, comprising:

a sugarcane harvester configured to harvest sugarcane;

a terahertz sensor including:

a terahertz source disposed to direct electromagnetic radiation towardthe sugarcane;

a terahertz detector disposed to detect the terahertz electromagneticradiation after the terahertz electromagnetic radiation interacts withthe sugarcane; and

a controller operably coupled to the terahertz detector, the controllerbeing configured to detect a harvest-related parameter based on a signalfrom the terahertz detector and to perform an action based on thedetected parameter.

Example 19 is the agricultural system of any or all previous examples ofany or all previous examples wherein the sugarcane harvester isconfigured to process the sugarcane into billets and wherein thecontroller is configured to detect, as the detected parameter, aparameter indicative of sugar content in the sugarcane billets.

Example 20 is the agricultural system of any or all previous exampleswherein the sugarcane harvester is configured to process the sugarcaneinto billets and wherein the controller is configured to detect, as thedetected parameter, a parameter indicative of an object in at least oneof the sugarcane billets.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. An agricultural system, comprising: anagricultural harvester; a terahertz sensor including: a terahertz sourcedisposed to direct electromagnetic radiation toward a harvest material;a terahertz detector disposed to detect the terahertz electromagneticradiation after the terahertz electromagnetic radiation interacts withthe harvest material; and a controller operably coupled to the terahertzdetector, the controller being configured to detect a harvest-relatedparameter based on a signal from the terahertz detector and to performan action based on the detected parameter.
 2. The agricultural system ofclaim 1 wherein the agricultural harvester comprises: a baler configuredto gather the harvest material from a field and process the gatheredharvest material to generate a bale of harvest material.
 3. Theagricultural system of claim 2 wherein the controller comprises: acontrol signal generator configured to generate a control signal toconfigure a data record corresponding to the bale, the data recordindicating that the bale includes the detected parameter.
 4. Theagricultural system of claim 2 wherein the control signal generator isconfigured to generate a control signal to configure a radio frequencyidentification tag corresponding to the bale and indicating that thebale includes the detected parameter.
 5. The agricultural system ofclaim 2 wherein the baler comprises: a cotton harvester configured toprocess the gathered harvest material to generate, as the bale, a moduleof harvested cotton.
 6. The agricultural system of claim 5 wherein thecontroller is configured to detect, as the detected parameter, aparameter indicative of cellulose in the module of harvested cotton. 7.The agricultural system of claim 2 wherein the controller comprises: acontrol signal generator configured to generate a control signal tocontrol the baler to eject the bale based on the detected parameter. 8.The agricultural system of claim 1 wherein the controller comprises: amachine speed detector configured to detect a ground speed of theagricultural harvester; and a deceleration signal generator configuredto generate a deceleration signal based on the detected parameter andthe ground speed of the agricultural harvester; and a control signalgenerator configured to generate a control signal to stop theagricultural harvester based on the deceleration signal.
 9. Theagricultural system of claim 8 wherein the controller comprises: amachine settings detector configured to detect a machine setting valueon the agricultural harvester; and a subsequent operation identificationsystem configured to identify a subsequent operation to perform based onthe detected parameter.
 10. The agricultural system of claim 9 whereinthe controller comprises: an operation resumption system configured toresume operation of the agricultural machine after the subsequentoperation is performed, based on the machine setting value on theagricultural harvester prior to performing the subsequent operation. 11.The agricultural system of claim 1 wherein the agricultural harvestercomprises: a sugarcane harvester configured to sever sugarcane and cutthe severed sugarcane into billets, wherein the terahertz sensor isconfigured to detect the harvest-related parameter in the sugarcane. 12.The agricultural system of claim 11 wherein the controller is configuredto detect, as the detected parameter, a parameter indicative of anobject in at least one of the sugarcane billets.
 13. The agriculturalsystem of claim 11 wherein the controller is configured to detect, asthe detected parameter, a parameter indicative of sugar content in thesugarcane billets.
 14. The agricultural system of claim 1 and furthercomprising: an unmanned aerial vehicle (UAV) configured to travel headof the agricultural harvester, the terahertz sensor being mounted to theUAV to detect the harvest-related parameter in the harvest materialahead of the agricultural harvester.
 15. An agricultural system,comprising: an agricultural harvester configured to process harvestmaterial to generate a bale or module of the harvest material; aterahertz sensor including: a terahertz source disposed to directelectromagnetic radiation toward the harvest material; a terahertzdetector disposed to detect the terahertz electromagnetic radiationafter the terahertz electromagnetic radiation interacts with the harvestmaterial; and a controller operably coupled to the terahertz detector,the controller being configured to detect a harvest-related parameterbased on a signal from the terahertz detector and to perform an actionbased on the detected parameter.
 16. The agricultural system of claim 15wherein the controller comprises: a machine speed detector configured todetect a ground speed of the agricultural harvester; and a decelerationsignal generator configured to generate a deceleration signal based onthe detected parameter and the ground speed of the agriculturalharvester; and a control signal generator configured to generate acontrol signal to stop the agricultural harvester based on thedeceleration signal.
 17. The agricultural system of claim 15 wherein thecontroller comprises: a control signal generator configured to generatea control signal to configure a data record corresponding to the bale ormodule, the data record indicating that the bale or module includes thedetected parameter.
 18. An agricultural system, comprising: a sugarcaneharvester configured to harvest sugarcane; a terahertz sensor including:a terahertz source disposed to direct electromagnetic radiation towardthe sugarcane; a terahertz detector disposed to detect the terahertzelectromagnetic radiation after the terahertz electromagnetic radiationinteracts with the sugarcane; and a controller operably coupled to theterahertz detector, the controller being configured to detect aharvest-related parameter based on a signal from the terahertz detectorand to perform an action based on the detected parameter.
 19. Theagricultural system of claim 18 wherein the sugarcane harvester isconfigured to process the sugarcane into billets and wherein thecontroller is configured to detect, as the detected parameter, aparameter indicative of sugar content in the sugarcane billets.
 20. Theagricultural system of claim 18 wherein the sugarcane harvester isconfigured to process the sugarcane into billets and wherein thecontroller is configured to detect, as the detected parameter, aparameter indicative of an object in at least one of the sugarcanebillets.